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A QUALITATIVE REASONING FRAMEWORK FOR THE SIMULATION OF SN1 AND SN2 MECHANISMS
IN ORGANIC REACTIONS
TANG YEE CHONG
THESIS SUBMITTED IN FULFILMENT OF THE REQUIREMENTS
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
FACULTY OF COMPUTER SCIENCE & INFORMATION TECHNOLOGY
UNIVERSITY OF MALAYA KUALA LUMPUR
FEBRUARY 2011
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Abstract
In organic chemical reactions, one has to understand the many cognitive steps involved
before a stable product is formed. Understanding these cognitive steps is among the
many difficulties faced by chemistry students. Traditional chemistry educational software
is inadequate in promoting understanding such as why and how things happen. These
programs do not “explain” simply because the results are obtained through chaining of
rules or by searching the reaction routes that have been pre-coded in software.
This thesis describes a qualitative reasoning framework for the simulation of SN1 and
SN2 mechanisms in organic reaction based on Qualitative Process Theory (QPT). The
modelling constructs of QPT provide grounds for representing chemical theories
qualitatively with notions of causality which can be used to explain the behaviour of a
chemical system. The major theme of this framework is that, in a qualitative simulation
environment, students are able to articulate his/her knowledge through the inspection of
explanations generated by software. These students are seen as the recipients of
knowledge delivered via the “explanation” pedagogy. To test the framework, a simulator
prototype, named QRiOM (Qualitative Reasoning in Organic Mechanism) was
implemented.
Specifically, this thesis investigates the qualitative reasoning approach and QPT ontology
applied to the task of constructing qualitative models and generating explanation for the
simulation of organic chemical reactions. The framework focuses on a few issues relating
to: (1) Automation of the qualitative model construction for organic reaction processes,
and (2) Improvement of the explanation generation approach since current chemistry
software cannot appropriately explain a chemical phenomenon. In this work, “make-
bond” and “break-bond” are identified as two generic processes in the simulation of
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organic reactions. From analysis of various chemical reactions occurring under SN1 and
SN2 mechanisms, the common set of chemical theories and behaviour for the generic
processes have been identified, from which the model automation procedures are
formulated. The issue of lack of explanation in chemistry software is addressed by
embedding a causal explanation generator that produces explanation in various forms.
The generator justifies and explains a simulated result by tracing the chains of causality
that stem from QPT model reasoning. These features are demonstrated via QRiOM.
Since QRiOM is developed to promote learners’ understanding of organic chemical
reactions, the effectiveness of QRiOM in explaining organic chemical phenomena has
also been evaluated. Evaluation results show that the tool has enhanced student
knowledge in organic chemical reactions and mechanisms.
This thesis comprises two main contributions. The first contribution is the application of
QPT to model various organic chemical reactions occurring under SN1 and SN2
mechanisms and to reproduce the chemical behaviour of the SN1 and SN2 mechanisms
“intuitively”. The thesis also provides justifications that QPT can be effectively used to
support learning. The second contribution is the development of an explanation module
obtained from the process model directly. This explanation module can be generalized
and used in other systems.
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Abstrak
Dalam reaksi kimia organik, kita harus memahami langkah-langkah kognitif yang terlibat
sebelum suatu produk yang stabil terbentuk. Memahami langkah-langkah kognitif
adalah salah satu masalah yang dihadapi oleh pelajar-pelajar kimia. Perisian kimia
tradisional untuk pembelajaran tidak mencukupi dalam meningkatkan pemahaman
seperti mengapa dan bagaimana sesuatu terjadi. Program-program ini tidak dapat
menjelaskan sesuatu koncep kerana keputusan yang diperolehi adalah melalui
penggunaan peraturan dan fakta atau dengan mencari laluan reaksi yang telah dikodkan
dalam perisian.
Tesis ini menggambarkan rangka kerja penaakulan kualitatif untuk mensimulasikan
mekanisme SN1 dan SN2 dalam reaksi organik berdasarkan Qualitative Process Theory
(QPT). Konstruk pemodelan yang terdapat pada QPT menyediakan asas untuk mewakili
teori kimia secara kualitatif yang boleh digunakan untuk menjelaskan perilaku sistem
kimia. Tema utama dari rangka kerja ini adalah bahawa, dalam lingkungan simulasi
kualitatif, pelajar mampu mengartikulasikan pengetahuannya dengan menyemak
penjelasan yang dihasilkan oleh perisian. Pelajar-pelajar ini dianggap sebagai penerima
pengetahuan yang disampaikan melalui pedagogi “penjelasan”. Untuk menguji rangka
tersebut, sebuah prototaip simulator bernama QRiOM (Qualitative Reasoning in Organic
Mechanism) telah dibangunkan.
Secara khusus, tesis ini meneliti pendekatan penaakulan kualitatif dan ontologi QPT
untuk membina model kualitatif dan simulasi untuk menghasilkan penjelasan untuk
reaksi kimia organik. Rangka kerja ini menumpukan pada beberapa isu berkaitan
dengan: (1) Pembangunan model kualitatif secara otomatik untuk reaksi organik, dan (2)
Peningkatan pendekatan dalam “penjelasan” kerana perisian kimia pada saat ini tidak
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dapat secara tepat menggambarkan fenomena kimia. Dalam kajian ini, “make-bond” dan
“break-bond” dikenalpasti sebagai dua proses generik dalam simulasi reaksi organik.
Dari analisis pelbagai reaksi kimia yang berlaku di mekanisme SN1 dan SN2, teori umum
dan perilaku untuk proses generik itu telah dikenalpasti, dari mana prosedur untuk
automasi model dirumuskan. Masalah kurangnya penjelasan dalam perisian kimia
diselesaikan dengan adanya sebuah generator “penjelasan kausal” yang menghasilkan
berbagai bentuk penjelasan. Generator tersebut menggambarkan dan menjelaskan hasil
simulasi dengan menelusuri rantai kausal dari model QPT. Ciri-ciri ini ditunjukkan
melalui QRiOM. Tujuan QRiOM adalah untuk meningkatkan pemahaman pelajar-
pelajar, oleh itu keberkesanan QRiOM dalam menjelaskan fenomena kimia organik telah
dinilai. Keputusan penilaian menunjukkan bahawa QRiOM dapat meningkatkan
pengetahuan pelajar dalam kimia organik dan mekanisme reaksi.
Tesis ini mempunyai dua sumbangan utama. Sumbangan pertama adalah penggunaan
QPT untuk pemodelan pelbagai reaksi kimia organik dalam mekanisme SN1 dan SN2, dan
mengeluarkan semula perilaku mekanisme SN1 dan SN2 secara “intuitif”. Tesis ini juga
memberikan justifikasi bahawa QPT boleh digunakan secara berkesan untuk menyokong
pembelajaran. Sumbangan kedua adalah pembangunan modul penjelasan dalam prototaip
QRiOM. Modul ini juga boleh digunakan dalam sistem lain.
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Acknowledgements
I would like to express my sincere gratitude to my supervisor, Dr. Rukaini Abdullah, for
her intellectual support, guidance and suggestions for improvements on my thesis. This
had a significant impact on this thesis. I would also like to thank her for offering
abundant ideas in fixing the structure of this thesis and for being very patient with my
progress.
My heartfelt thanks are extended to Professor Dr. Sharifuddin Mohd Zain for being a
great supervisor and friend. Many thanks are due for his insightful comments, research
perspective, and encouragement throughout the period of this research. I feel extremely
fortunate to have him as my supervisor.
I would also like to express my sincere appreciation to Professor Dr. Noorsaadah Abdul
Rahman. I can never forget the several extremely valuable discussions we had during the
undertaking of this research. I can still remember the first time I studied organic
chemistry where I could not differentiate between nucleophiles and electrophiles. I would
like to thank her for having confidence and trust in me. The support from my three
supervisors formed the kernel around which this thesis has developed.
A special gratitude is extended to Associate Professor Dr. S.M.F.D. Syed Mustapha for
introducing me to the wonders of qualitative reasoning. His contributions to this work
include the time he devoted in guiding me and the expertise he shared with me at the
beginning of this research work.
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I would also like to thank the following colleagues of mine for their motivation and help:
• Dr. Sharifah Mumtazah Syed Ahmad (Systems & Networking Department, Head) –
for her full support to enable me to complete this work at the earliest possible time.
• Assoc. Prof. Dr. Siti Salbiah (College of Information Technology, Dean) – for
approving several sponsorships to enable me to present papers at overseas
conferences.
• Dr. Abdul Rahim Ahmad (College of Information Technology, Deputy Dean) – for
his willingness and kindness to share his research thoughts with me.
• Dr. Chai Mee Kin – who helped me in the earlier part of this research where I needed
expertise in the domain of inorganic and organic chemistry.
• Asma Shakil – for proofreading a few chapters of this thesis.
• My colleagues in College of Engineering, Science & Mathematics department – for
stimulating many interesting ideas in my work.
Lastly, closer to home, I am most grateful to my family members for their love and
support throughout the long process required to complete my PhD. My deepest gratitude
goes to my father for his encouragement during the course of this research.
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Dedication
This thesis is dedicated to a woman of strength and wisdom, who wished to see me reach
this point in my education path. She is my late mother – Madam H.M. Chua.
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Table of Contents
Abstract..............................................................................................................................ii
Abstrak..............................................................................................................................iv
Acknowledgements...........................................................................................................vi
Dedication.......................................................................................................................viii
Table of Contents.............................................................................................................ix
List of Figures.................................................................................................................xiii
List of Tables....................................................................................................................xx
List of Abbreviations.....................................................................................................xxii
Chapter 1 Introduction .................................................................................................... 1
1.1 Introduction ......................................................................................................... 1
1.2 Background Review ............................................................................................ 2
1.2.1 Qualitative Reasoning ............................................................................... 2
1.2.2 Qualitative Process Theory (QPT) ............................................................ 8
1.2.3 Organic Reaction and Organic Mechanism ............................................. 12
1.3 Problem Statement ............................................................................................ 17
1.4 Objectives ......................................................................................................... 19
1.5 Research Questions ........................................................................................... 20
1.6 Scope of Research ............................................................................................. 25
1.6.1 System Scope........................................................................................... 25
1.6.2 Course Scope ........................................................................................... 26
1.7 Main Results ..................................................................................................... 26
1.8 Thesis Structure ................................................................................................ 27
Chapter 2 Literature Review......................................................................................... 31
2.1 Introduction ....................................................................................................... 31
2.2 Review of the Literature on Qualitative Reasoning Applications .................... 31
2.2.1 In Industry ............................................................................................... 32
2.2.2 In Education............................................................................................. 33
2.3 Review of the Literature on Work Using Qualitative Process Theory ............. 36
2.4 Analyzing Domain Suitability .......................................................................... 38
2.4.1 Explaining Organic Chemical Reactions in the Classroom .................... 40
2.5 Use of Artificial Intelligence in Organic Chemistry ......................................... 40
2.5.1 The Traditional Knowledge-based Approach ......................................... 41
2.5.2 The Machine Learning Approach ............................................................ 41
2.6 Molecular Representation Schemes .................................................................. 43
2.6.1 The Simplified Molecular Input Line Entry System (SMILES) Codes .. 43
2.6.2 International Chemical Identifier (InChI) ............................................... 44
2.7 Related Works ................................................................................................... 45
2.7.1 LHASA ................................................................................................... 46
2.7.2 QALSIC ................................................................................................... 47
2.7.2.1 Limitations and Problems in the QALSIC Program ................. 48
2.7.2.2 Organic Reactions Versus Inorganic Reactions ........................ 50
2.7.2.3 Inorganic Reactions in Qualitative Reasoning: The Problems . 52
2.7.2.4 Discussion ................................................................................. 58
2.8 Conclusion ........................................................................................................ 59
Chapter 3 Qualitative Modelling of Organic Reactions ............................................. 60
3.1 Introduction ....................................................................................................... 60
3.2 State of the Art in Qualitative Modelling ......................................................... 61
3.3 Domain Knowledge Acquisition ....................................................................... 64
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3.4 Understanding Organic Chemistry Reactions ................................................... 65
3.5 Organic Reaction as Modelling Task ................................................................ 67
3.5.1 Chemical Equation as a Reasoning Task................................................. 69
3.6 The Underlying Thought Processes for Organic Reactions .............................. 70
3.6.1 Individual Views Identification ............................................................... 75
3.6.2 Representing Individual Views ............................................................... 77
3.6.3 Relation Between View Pairs and Organic Processes ............................. 79
3.7 Reaction Steps Classified as “make-bond” and “break-bond” Processes ........ 81
3.7.1 Proof of Common Behaviour Exhibited in Organic Processes ............... 82
3.7.1.1 Behaviour Generalization for “make-bond” Process ................ 84
3.7.1.2 Behaviour Generalization for “break-bond” Process ................ 87
3.8 Representing Organic Chemistry Theories Using QPT Constructs .................. 90
3.8.1 Direct and Indirect Influences in Organic Reaction Simulation ............. 91
3.8.2 Postulating Limit Points .......................................................................... 91
3.8.3 The Quantities and Quantity Spaces........................................................ 94
3.9 Useful Guidelines in Modelling Views for Organic Reactions ........................ 95
3.10 Learning with Qualitative Models .................................................................... 98
3.10.1 Ontology Primitives as Explanation Facilitator .................................... 99
3.10.2 Learning Activities Manifestation ....................................................... 100
3.11 Conclusion ...................................................................................................... 102
Chapter 4 Qualitative Simulation and Explanation Generation ............................. 103
4.1 Introduction ..................................................................................................... 103
4.2 State of the Art in Qualitative Simulation and Explanation in Education ...... 104
4.3 Qualitative Simulation Scenario ..................................................................... 109
4.3.1 An Overview of the Simulation Architecture ........................................ 110
4.3.2 Reproducing Behaviour of Organic Reactions via QPT Reasoning ..... 113
4.4 Chemical Behaviour of SN1 and SN2 Mechanisms ......................................... 115
4.4.1 The SN1 Mechanism .............................................................................. 116
4.4.2 The SN2 Mechanism .............................................................................. 120
4.5 Simulation Scenario for Reproducing the Behaviour of SN1 .......................... 121
4.5.1 Contents of the View Instance Structure (VIS) During Reasoning ....... 124
4.5.2 Stopping Conditions for Reaction Steps and the Entire Simulation...... 125
4.6 QPT Process Model as Reusable Component ................................................. 126
4.6.1 Model Reuse by SN2 .............................................................................. 128
4.6.2 Model Reuse Scenario ........................................................................... 129
4.7 Qualitative Explanation Manifestation ........................................................... 131
4.7.1 Generating a Causal Graph .................................................................... 132
4.7.2 Design of Causality ............................................................................... 134
4.7.3 Interpreting a Causal Graph................................................................... 138
4.7.4 Deriving Explanation From a Causal Graph ......................................... 140
4.8 Discussion ....................................................................................................... 141
4.9 Conclusion ...................................................................................................... 142
Chapter 5 Qualitative Reasoning Framework for Organic Reaction Simulation .. 144
5.1 Introduction ..................................................................................................... 144
5.2 The Qualitative Reasoning Framework .......................................................... 145
5.2.1 Inputs ................................................................................................. 145
5.2.2 Outputs ................................................................................................. 146
5.2.3 Software Components ........................................................................... 146
5.3 Component Design .......................................................................................... 152
5.3.1 The Two-tier Architecture of Knowledge Base .................................... 152
5.3.2 The Chemical Knowledge Base ............................................................ 154
5.3.3 OntoRM: Objectives and Motivations ................................................... 155
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5.3.3.1 The Design of OntoRM .......................................................... 156
5.3.3.2 Validation Examples ............................................................... 161
5.3.4 The Substrate Recognizer ...................................................................... 165
5.3.5 The Model Constructor for Organic Processes ..................................... 166
5.3.6 The Reasoning Engine for Reaction Simulation ................................... 167
5.3.7 The Causal Model Generator ................................................................. 168
5.4 Storing Molecular Patterns in Software .......................................................... 170
5.4.1 Design of Attributes and Methods for an Atom .................................... 171
5.4.2 Connection Table................................................................................... 172
5.4.3 The Molecule Table ............................................................................... 173
5.5 Knowledge Structuring ................................................................................... 175
5.6 The Protocol for Interacting with QRiOM ...................................................... 178
5.7 Simulation Results and Discussion ................................................................. 180
5.7.1 Reaction Route ...................................................................................... 182
5.7.2 QPT Model ............................................................................................ 184
5.7.3 Causal Graph ......................................................................................... 185
5.7.4 Parameter State History and Atom Property Tables .............................. 186
5.7.5 List of Reacting Species (View Pairs) ................................................... 187
5.8 Conclusion ...................................................................................................... 188
Chapter 6 Evaluation of QRiOM ................................................................................ 190
6.1 Introduction ..................................................................................................... 190
6.2 The Evaluation Context .................................................................................. 190
6.3 Procedures Used for Conducting the Questionnaires ..................................... 192
6.3.1 Students’ Feedback on the use of QPT and Qualitative Reasoning Approaches ............................................................................................ 195
6.3.2 Assessment of Students’ Skills in Core Areas of Organic Reactions – The Pre-Questionnaire .................................................................................. 197
6.3.3 Assessment of Students’ Skills in Core Areas of Organic Reactions – The Post-Questionnaire ................................................................................ 198
6.3.4 Assessment of Effectiveness of QRiOM’s Explanation Facility .......... 200
6.3.5 Assessment of the Usefulness and Helpfulness of QRiOM .................. 202
6.3.6 Comments on Graphical User Interface Design .................................... 204
6.4 Conclusion ...................................................................................................... 205
Chapter 7 Conclusion ................................................................................................... 207
7.1 Thesis Summary .............................................................................................. 207
7.2 Results and Contributions ............................................................................... 209
7.2.1 Conceptual Framework Development ................................................... 210
7.2.1.1 QPT as the Knowledge Capture Tool ..................................... 211
7.2.1.2 Model Automation .................................................................. 212
7.2.2 QRiOM – A Tool for Explaining Organic Reactions ............................ 213
7.2.3 Evaluation Results of QRiOM............................................................... 214
7.3 Limitations ...................................................................................................... 217
7.4 Future Works .................................................................................................. 217
7.5 Concluding Remarks ....................................................................................... 219
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References.........................................................................................................................220 Appendix A: A Summary of Systems Related to Qualitative Reasoning........................230 Appendix B: Collection of Flowcharts for the Qualitative Reasoning Framework........237 Appendix C: Questionnaires Used for Collecting Students’ Feedback on the use of QPT
and Qualitative Reasoning Approaches ....................................................243 Appendix D: Selected Computer Screenshots.................................................................246 Appendix E: Program Snippets for the Main Software Modules in QRiOM
....................................................................................................................259
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List of Figures Figure Page
1.1 A brick and an elastic string tied up at one end for demonstrating qualitative
reasoning technique. ......................................................................................... 4 1.2 A general view of qualitative reasoning. .......................................................... 7 1.3 A general view of QPT. .................................................................................. 10 1.4 A “charge” quantity and its space. .................................................................. 10 1.5 The five slots of a QPT process. ..................................................................... 11 1.6 Students’ barriers to understanding the organic chemistry course. ................ 15 1.7 Thesis layout. .................................................................................................. 30 2.1 Some of the benefits of applying qualitative reasoning to a chemical system
simulation. ....................................................................................................... 42 2.2 A proposed scheme to classify inorganic experiment types. .......................... 50 3.1 Simulation entails reasoning from model. ...................................................... 64 3.2 The conversion of a tertiary alcohol to yield alkyl chloride can be described as
a series of three small steps. ............................................................................ 72 3.3 The production of a tertiary alcohol can be described as a series of three
reaction steps. .................................................................................................. 74 3.4 The “dissociation” and “reaction with HO−−−−” are concerted steps. This is a
typical SN2 backside attack reaction. .............................................................. 75 3.5 (a) Generic definition for an electrophile described using QPT (b) An
electrophile used in “make-bond” process (c) An electrophile used in in “break-bond” process. ..................................................................................... 78
3.6 (a) Generic definition for a nucleophile described using QPT (b) A
nucleophile used in “make-bond” process (c) A “delta-minus” view. It is used when the covalent bond between a delta-plus and a delta-minus species is deleted. ........................................................................................................ 79
3.7 An instantiated “make-bond” process described using QPT modelling
constructs. The process focuses on the nucelophile (the “OH”) to be replaced and the proton. ................................................................................................ 89
3.8 An instantiated “break-bond” process described using QPT modelling
constructs. The process focuses on the leaving group and the electrophilic carbon centre. .................................................................................................. 90
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3.9 Alcohol reactivity under SN1 mechanism. ...................................................... 97 3.10 The QPT process specification that models the behaviour of a “make-bond”
process. ............................................................................................................ 99 4.1 The use of qualitative reasoning, simulation and explanation within the
context of this work. ..................................................................................... 109 4.2 Workflow of the QPT-based reasoning. ....................................................... 112 4.3 A “make-bond” model fragment represented using QPT. This model fragment
is used to reproduce the behaviour of the first reaction step for “(CH3)3C–OH + HCl” reaction. ............................................................................................ 114
4.4 Reaction between the hydroxide ion (OH−−−−) and a tertiary halide. ................ 116 4.5 The stability of various structures of a carbocation under SN1 mechanism. . 118 4.6 A reaction that needs SN1. ............................................................................. 119 4.7 The organic processes occurred in the order of “make-bond” (Step 1), “break-
bond” (Step 2) and “make-bond” (Step 3). The reaction can be explained by the SN1 mechanism. ...................................................................................... 120
4.8 A “break-bond” model fragment represented using QPT. This model fragment
is used to reproduce the behaviour of the second step of “(CH3)3COH + HCl”.. ............................................................................................................ 123
4.9 A “make-bond” model fragment represented using QPT. This model fragment
is used to reproduce the behaviour of the third step of “(CH3)3COH + HCl”...............................................................................................................124
4.10 The contents in VIS during the simulation of “protonation” process. The VIS
is constantly updated to reflect the new intermediates produced until the entire reaction is ended. Content in (d) is the final product. ................................... 125
4.11 The contents in VIS during the simulation of the “dissociation” process.
Content in (d) is the final product of this reaction. ....................................... 125 4.12 The QPT process models constructed for Equation 3.2 can be reused by other
chemical equation simulation such as Equation 4.3. .................................... 127 4.13 The mechanism used in this simulation is SN2. The organic processes that
occurred are “break-bond” (expulsion of the leaving group) and “make-bond” (the approaching of the hydroxide ion to form a bond to the carbon centre).128
4.14 Model reuse scenario for the simulation of organic reactions. ..................... 130 4.15 (a) A problem solving method that uses concepts to tackle multiple problems
(b) A precoded KB of an expert system in solving a specific problem. ....... 131
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4.16 A causal graph showing cause-effect relationship of chemical parameters during the simulation of “(CH3)3C–OH + HCl” reaction. ............................ 133
4.17 Causal graph for the “protonation” process. The inequality above the dotted
line is the entry condition to the process. ...................................................... 135 4.18 Causal graph for the “dissociation” process. The process stops when the
oxygen (“O”) regains its equilibrium state. .................................................. 137 4.19 Causal graph for the “Capturing of carbocation by anion” process. ............. 137 5.1 A schematic view of the qualitative reasoning framework described in terms
of the input, process, output and the knowledge bases. ................................ 147 5.2 Main software components of QRiOM. ........................................................ 148 5.3 Architectural design of the knowledge base. ................................................ 153 5.4 Examples of chemical facts and theories used in reaction simulation...........154 5.5 Basic concepts in OntoRM ontology are hierarchically structured using the
IS-A relation. ................................................................................................. 159 5.6 Properties of basic concepts defined in the ontology are encapsulated in the
format of a Java class. ................................................................................... 160 5.7 Chemical properties of SN1 and SN2. ............................................................ 160 5.8 The main steps in the model constructor module..........................................167 5.9 The main steps of the simulation algorithm. ................................................. 168 5.10 Main steps in the QSA module. .................................................................... 169 5.11 The flowchart for generating a causal graph. ................................................ 170 5.12 A substrate’s functional group represented as a connection table. ............... 172 5.13 Connection table for initial structure of the substrate. .................................. 173 5.14 Connection table after the “protonation” process (“make-bond”). The digit
“1” is filled in the correct entry based on the individuals that activates the process. “H2” indicates the newly added atom. ............................................ 173
5.15 Algorithmic steps in the MUR module that updates the molecule table in
order to prepare the reaction route of a chemical reaction. ........................... 174 5.16 A molecule table is represented as a 2D array. This is the initial structure of
the alcohol substrate. ..................................................................................... 174 5.17 The “H” has been attached to the main compound. This is the effect of the
generic “make-bond” process. ...................................................................... 175
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5.18 Protocol in using the simulator (Labels A – H can be found in Figure 5.19)...............................................................................................................179
5.19 Main interface of the QRiOM software. ....................................................... 180 5.20 Screenshots showing two reaction routes generated by QRiOM at the end of a
simulation. ..................................................................................................... 183 5.21 A computer generated QPT model. .............................................................. 184 5.22 A causal graph generated by QRiOM that enables learners to examine the
cause-effect relationships of chemical parameters during reasoning............ 185 5.23 The states of chemical parameter of each reacting species involved in a
simulation task can be examined in greater detail. ....................................... 186 5.24 (a) The chemical states possessed by each reacting unit during simulation are
stored in the atom property table (b) A reaction route drawn from using the data values in the atom property table. ......................................................... 187
5.25 The choice of reacting units for each reaction step and the intermediates
produced are displayed for further inspection............................................... 188 6.1 Flowchart of the QRiOM evaluation exercise. ............................................. 194 6.2 Examples of survey questions used for measuring students’ understanding
towards QPT. ................................................................................................ 195 6.3 Sample questions in a survey form that collect students’ opinions about
qualitative reasoning and modelling approaches. ......................................... 196 6.4 Students’ responses towards understanding QPT and qualitative reasoning
approaches. .................................................................................................... 197 6.5 The survey form for course competency assessment distributed before/after
using the simulator. ....................................................................................... 198 6.6 Student pre-test and post-test responses to the core skills. ........................... 199 6.7 Questions in the survey form for the measure of explanation-based learning in
skills reinforcement. ...................................................................................... 200 6.8 Students’ feedbacks on the extent to which the tool improves one’s
knowledge in terms of skill reinforcement through explanation-based learning. ........................................................................................................ 201
6.9 Examples of the survey questions for the measure of usefulness and
helpfulness of QRiOM in a student’s learning endeavour. ........................... 202 6.10 Students’ feedbacks on helpfulness (motivated) and usefulness (gain more
confidence) of QRiOM. ................................................................................ 204
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7.1 Accomplishment of the QR approach when implemented in a tool for learning organic reactions. .......................................................................................... 216
B.1 Workflow of the QPT-based modelling, reasoning and explanation
framework……………………………………………..……………….......238 B.2 The task performed by the “Substrate Recognizer”……………..……...….239 B.3 Workflow for automating QPT model for organic
processes..…………………………………………………...…….……..…240 B.4 Workflow of the QPT-nased simulation and the micro steps in the QSA
module...........................................................................................................241 B.5 Workflow of the technique used in handling and generating an
explanation....................................................................................................242 C.1 Questionnaire to assess students’ understanding on QPT.............................244 C.2 Questionnaire to collect students’ opinions on qualitative modelling and
reasoning approaches of problem solving for organic chemistry.......................................................................................................245
D.1 Login page ....................................................................................................247 D.2 Front page of the QRiOM qualitative simulator ...........................................247 D.3 Main interface of QRiOM.............................................................................248 D.4 More learning activities and explanation can be viewed by clicking A, B and
C buttons…....................................................................................................248 D.5 Reaction route for the simulation of “CH3Cl + HO−” is
formed............................................................................................................249 D.6 Reaction route for the simulation of “CH3CH3CH3Br + H2O” is
formed............................................................................................................249 D.7 Reaction route for “CH3CH3CH2Cl + HO−”...............................................250 D.8 QPT model inspection page...........................................................................250 D.9 A “make-bond” process described in QPT terms (between a charged
nucleophile and a charged electrophile)........................................................251 D.10 A causal graph showing the cause and effect relationships of the various
chemical parameters during qualitative reasoning........................................251 D.11 Causal graph inspection page with annotation..............................................252
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D.12 Brief explanation of each slot in a QPT model.............................................252 D.13 More explanation for the various modelling constructs of QPT...................253 D.14 Contents of the View Instance Structure (VIS) give the pairs of reacting
species used in each small reaction step........................................................253 D.15 A snapshot of the contents of the VIS during the simulation of
“CH3CH3CH3COH + HBr”.........................................................................254 D.16 Each chemical state change (parameter state history) is recorded for further
examination...................................................................................................254 D.17 Chemical states for “HO-”are retrieved and displayed.................................255 D.18 Contents in the “substrate table” showing the functional units involved in a
reaction..........................................................................................................255 D.19 The screenshot for a specific case where QRiOM is unable to predict the
output, where the reason is displayed via a pop-up window.........................256 D.20 A screenshot of “no reasoning” for an input pair of <CH3Cl, HF>, where the
system simply returns a short message..........................................................256 D.21 A QPT learning corner is included in the software………………………...257 D.22 A “terminology help window” that provides quick notes for important organic
chemistry terms used in simulation and explanation.....................................257 D.23 The main interface for “model building” by the students – for future
expansion of the tool.....................................................................................258 D.24 Knowledge base Editor – for adding/deleting chemical facts and
theories..........................................................................................................258 E.1 The Java code for retrieving chemical facts of reacting species and for
constructing a QPT process...........................................................................261 E.2 The associated Java statements for updating the VIS in order to suggest the
next organic process in the qualitative simulation environment...................262 E.3 The Java code for updating the chemical parameters’ states of each atom
during simulation...........................................................................................263 E.4 The Java statements for constructing a causal graph.....................................264 E.5 The Java statements for retrieving the parameter history of a reacting
unit.................................................................................................................265 E.6 A sample set of definitions for nucleophiles, electrophiles and the basic
concepts of organic mechanisms...................................................................267
xix
E.7 A Java method that checks the nucleophilic reactivity for a pair of nucleophiles for possible substitution...........................................................268
E.8 A Java method that checks whether a substrate can undergo SN1 or SN2.....269 E.9 A Java method that checks the types of individual views in order to
recommend a suitable chemical process........................................................269 E.10 The associated Java statements to stop the entire reaction simulation..........270 E.11 The Java statements for displaying the organic processes in the order of
occurring........................................................................................................271 E.12 The Java statements to display the final product...........................................271
xx
List of Tables
Table Page
1.1 Some notations and semantics of the QPT modelling constructs. .................. 11 1.2 Relationships between research problems and questions, objectives and the
corresponding thesis chapters that answered them. ........................................ 24 2.1 Some examples of SMILES codes. ................................................................. 44 2.2 Comparison of InChI to SMILES formats. ..................................................... 45 2.3 Comparison of the actual and simulated results for a selected sample of
inorganic chemistry reactions. ........................................................................ 49 3.1 Relationship between view pair and covalent bonding. .................................. 80 3.2 A summary of the covalent bonding needed by three chemical equations
presented in this thesis. ................................................................................... 83 3.3 Reacting species and their chemical changes in the “protonation” process
(“make-bond”) of Equation 3.2. ...................................................................... 84 3.4 Reacting species and their chemical changes in the “capturing of halide anion
by carbocation” process (“make-bond”) of Equation 3.2. .............................. 84 3.5 Reacting species and their chemical changes in the “reacts with water”
process (“make-bond”) of Equation 3.3 for the formation of alcohol. ........... 85 3.6 Reacting species and their chemical changes in the “nucleophile attacks”
process (“make-bond”) of Equation 3.4 for the formation of ethanol. ........... 85 3.7 The reacting species involved in this “break-bond” process are “C” from the
alkyl group and the “O” from the oxonium ion. The carbon is δ+, so that the electrons are pushed towards “O” which is more electronegative. ................. 87
3.8 In this “break-bond” process, the atoms involved are “C” and “Br” from the
same molecule. ................................................................................................ 88 3.9 In this “break-bond” process, the atoms involved are “C” and “Br”. Bromine
is more electronegative than the other hygrogen substituents. So, it is the Br that leaves the molecule. ................................................................................. 88
3.10 Quantity spaces and limit points for the three main quantities used in the
framework. ...................................................................................................... 91 3.11 Examples of quantities and associated quantity spaces. ................................. 95 4.1 A set of queries and explanations. The explanation is generated based on Step
1 in the causal graph presented in Figure 4.16. ............................................. 140
xxi
4.2 A set of queries and explanations. The explanation is generated based on the second step of the causal graph presented in Figure 4.16. ............................ 141
5.1 Main modules and their roles. ....................................................................... 149 5.2 Software modules and the associated inputs and outputs. ............................ 151 5.3 Data types and the associated values. ........................................................... 161 5.4 Some attributes and methods associated with an atom. ................................ 171 5.5 Three abstraction levels of knowledge for use in QRiOM. .......................... 176 5.6 Knowledge types, abstraction levels and roles for use in QRiOM. .............. 177 5.7 Computer screenshots, objectives and the questionnaires used to test it. ..... 182 6.1 Questionnaires and the fulfilment of respective educational objective. ....... 193
A.1 Examples of educational software employing QR approaches......................231
xxii
List of Abbreviations
AI Artificial Intelligence
CAD Computer-Aided Design
CHMTRN Chemistry Translation
ES Expert Systems
GUI Graphical User Interface
InChi International Chemical Identifier
IT Information Technology
ITS Intelligent Tutoring Systems
IUPAC International Union for Pure and Applied Chemistry
KB Knowledge Base
KBS Knowledge-Based Systems
LG Leaving Group
LHASA Logic and Heuristics applied to Synthetic Analysis
MUR Molecule Update Routine
OntoRM Ontology for Reaction Mechanisms
OOP Object Oriented Programming
QPT Qualitative Process Theory
QR Qualitative Reasoning
QRiOM Qualitative Reasoning in Organic Mechanisms
QSA Quantity Space Analyzer
SMILES Simplified Molecular Input Line Entry System
SOM Self-Organizing Map
SN1 Single Molecular Nucleophilic Substitution
SN2 Bi Molecular Nucleophilic Substitution
VIS View Instance Structure
1
Chapter 1 Introduction
1.1 Introduction
In the past, simulations are based on complex mathematical procedures. These
procedures are used for calculating how the specific aspects within the simulation are to
be manipulated. Numerical analysis based on mathematical models provides no
conceptual access to the objects and their behaviour in the simulation. It is not possible
to derive causal explanation of the behaviour of a particular system from the
mathematical models. As a result, approaches based on mathematical models are not
suitable for inclusion in learning tools for many science subjects such as organic
chemistry. A substantial body of research in Qualitative Reasoning (QR) has shown
that many powerful reasoning can be done with only partial or less detailed knowledge
and without using mathematical models with differential equations (Iwasaki, 1997).
QR would be able to make acceptable predictions using only qualitative information
about new situations. Even though QR has been around for many years, no one has
reported work on organic reaction simulation using the QR technology.
There has been many strives for innovation in teaching and learning chemistry using
computer software. However, most of the chemistry educational software used
traditional approaches (Cartwright, 1993). In the standard rule-based systems,
explanation is generated by tracing all the rules that executed during a search for
solution. As such, these systems are incapable of providing behavioural types of
explanation on demand such as explaining why things happen and how they happen.
The programs were often difficult and time consuming to learn to use. To improve
understanding in chemistry, chemical knowledge and chemical commonsense can be
2
represented using an appropriate ontology (as the knowledge representation tool) for
reasoning and simulation use. The reasoning approach referred here is QR and the
appropriate ontology is Qualitative Process Theory (Forbus, 1984), a process-based QR
ontology. As there is a strong link between mental model and knowledge representation,
this relationship can help build new kind of educational software for teaching science
subjects such as organic chemistry more effectively. In view of this, the QR approach
based on qualitative process theory was investigated and applied to the problem domain
described in this work.
1.2 Background Review
1.2.1 Qualitative Reasoning
Qualitative Reasoning (QR) is an area of research combining Artificial Intelligence (AI)
and cognitive science. Briefly, AI is an attempt to reproduce intelligent reasoning using
machines while cognitive science is the study of the human mind (thought). The field
of cognitive science overlaps AI. Cognitive Science is an interdisciplinary field that has
arisen during the past decade at the intersection of a number of existing disciplines,
including psychology, linguistics, computer science, philosophy, and physiology. The
study of QR was originally motivated by observing human reasoning. For example,
people who do not know differential equations reason about many physical phenomena
perfectly well. Scientists and engineers also rely on simpler, qualitative models when
interpreting data at an initial stage of a design. In numerical simulation, many of the
processes are characterized by differential equations that describe how the parameters of
objects are changed over time. However, the notion of “process” is more structured than
the appearance of the set of equations itself.
3
One of the main goals of qualitative reasoning research is to formalize the rules people
use to mentally simulate the behaviour of a system through time. The technique was
initially developed to model commonsense reasoning and human-like reasoning within
the physical world. Commonsense is not something that can be easily explained to
computers. One of the greatest French philosophers and writers, Voltaire (1694–1778)
once said “commonsense is not so common” and that it is even more difficult to
formalize it for representation in computers. In 2004, Kuipers interviewed by Ubiquity
(Web-based publication of the Association for Computing Machinery,
http://www.acm.org/ubiquity/interviews/v4i45_kuipers.html) gave his novel perspective
on what he sees as “commonsense” knowledge. He defines commonsense as:
“…knowledge about the structure of the external world that is acquired and
applied without concentrated effort by any normal human that allows him or her
to meet the everyday demands of the physical, spatial, temporal and social
environment with a reasonable degree of success.”
According to Kuipers, if computational models of the human mind need to be built, then
the formalization of the kind of commonsense needed would have to be worked out. He
added that, people will not have to argue about exactly what “commonsense” means,
just as biologists seldom argue about exactly what “life” means. One of the most
notable things about human commonsense is people’s ability to make sensible
judgments even when they do not know all the relevant information about a situation.
This is the so-called “incomplete knowledge”. Kuipers believes that part of the power
of human commonsense knowledge comes from the ability to represent and use
knowledge even when it is incomplete. Commonsense knowledge is also knowledge of
certain domains that children learn about at a young age (e.g. space, time, the conditions
and results of actions, objects and their properties and the properties of materials).
Based on his interpretation and description of the term “commonsense”, the domain
4
knowledge required to solve the organic reaction problem described in this thesis is
represented as mental models having only partial knowledge.
The understanding of commonsense reasoning would require the study of how to reason
qualitatively about processes, namely, the kinds of changes that occur and their effects.
The central role is played by qualitative simulation, i.e. the prediction of possible
behaviours consistent with the incomplete knowledge of the structure of the physical
system. Adopting the “a brick and an elastic string” example from Forbus (1984),
Figure 1.1 illustrates a physical situation that portrays a brick and an elastic string tied
up at one end. This example needs commonsense knowledge and qualitative
representation to represent its behaviours. By qualitative reasoning, some of the
conclusions that can be drawn from Figure 1.1 are as follows:
• Question 1: “What if it gets pumped?” A possible conclusion could be “If there is
no friction the elastic spring will eventually break. If there is friction and the driving
energy is constant then there will be a stable oscillation”.
• Question 2: “What happens if we let go the block?” A possible conclusion would
be “Assuming the elastic spring does not collapse, the block will oscillate back and
forth and if there is friction it will eventually stop”.
Figure 1.1 A brick and an elastic string tied up at one end for demonstrating qualitative reasoning technique.
The above explanation is reasonable and much like the way a human interprets and
concludes what he/she observes. People can simulate these kinds of dynamical systems
with purely qualitative (symbolic) knowledge. This type of reasoning happens without
5
processing formal scientific theories that encompasses the relevant features in a detailed
and numerical means (or the specific numbers that would be required for a
mathematical model to run). Notice also that the commonsense conclusions can be
drawn without involving any mathematical expression such as a physical law like F =
m.a. where, m is mass; a is acceleration, and F represents force. From the equation, it
can be easily seen that increasing values of a are followed by increasing value of F.
However, the mathematical equation itself captures nothing about this important notion.
This is because in quantitative problem solving the representation of a system is a set of
mathematical formulas expressing the relations between the different parameters in the
system, without the representation of causality and physical structure. Given a set of
relations, there is no knowledge available about how the parameters relate to the
physical organization of the system, that is, topological structure is not explicit. QPT
(Section 1.2.2) offers the necessary modelling constructs to support notions which are
implicit in the equation such as to show that F and a have some kind of monotonic
relationships.
Education has the most links to QR research. An overview of QR research has been
discussed in Bredeweg and Struss (2003) while an analysis of QR in education can be
found in Bredeweg and Forbus (2003). As noted earlier, symbolic reasoning is among
the fundamental capabilities of human intelligence. Systems based on qualitative
reasoning are expected to possess the ability to predict and explain the behaviour of
physical systems in qualitative terms without involving mathematical equations. Many
application areas can benefit from QR approaches. Some advantages are: (1) The ability
to cope with incomplete information, (2) The ability to return imprecise but correct
prediction, (3) The ability to provide exploration of alternatives, and (4) It has inherent
automatic interpretation. Recently, a new generation of QR related tools have been
6
developed. These include software tools for domains such as ecology, engineering,
spatial data mining, information technology services, strategy game, Web services,
chemistry and the building of articulate software for commercial and educational uses.
A review of the literature on qualitative reasoning applications, qualitative process
theory application in education and training is given in Section 2.2, Chapter 2.
Several ontologies for qualitative reasoning have been introduced in 1980’s. Among
the well-known QR ontologies are process-centred (Forbus, 1984), component-based
(de Kleer and Brown, 1984) and constraint-based (Kuipers, 1986) approaches. These
languages provide new capabilities for science education software. By embedding
human-like models of entities and processes in the software, explanations that are
directly coupled to how specific results were derived can be provided. Although the
QR field has addressed diverse problem areas and developed a variety of theories and
systems, there are several features that are typical for many of the approaches and
theories. A general view of QR approaches and their typical features is presented in
Figure 1.2. Some of the most important ones are described in Bredeweg and Struss
(2003). The following five typical features are used in this work:
• Qualitative reasoning provides explicit representations of the conceptual knowledge,
and it requires ontologies to support its knowledge representation. This layer of
representation is crucial to any attempt to support model building and even more to
automate it. The two families of ontology are (1) interacting processes, and (2)
interconnected components.
• Explaining the behaviour of a system in terms of their cause-effect chain (more
commonly termed as causality). QR formalisms which make causality explicit are
of value in education (Forbus and Gentner, 2009).
7
• Most QR systems adopt a reductionist view of the world and aim at building
libraries of elementary, independent model fragments (e.g. processes, component
behaviour). This approach is called compositional modelling, which provides basis
for reusing models, a desirable features for many industrial applications.
• Include only those distinctions in a behaviour model that are essential for solving a
particular task. The goal is to obtain a finite representation that leads to coarse,
intuitive representations of models (not a detailed design with complete set of
numerical data). This feature is termed as “qualitativeness”.
• Inference of behaviour from structure.
(a) Main approaches of qualitative reasoning
(b) Typical features of qualitative reasoning
Figure 1.2 A general view of qualitative reasoning.
Component-based Process-based Constraint-based
de Kleer & Brown’s confluence-based qualitative physics E.g. Models of automotive electronics (Struss and Price, 2004)
Forbus’s qualitative process theory E.g. CyclePad (Forbus et al., 1999) and Garp workbench (Bredeweg et al., 2007)
Kuipers’s QSIM where qualitative mathematics is used directly in simulation E.g. Error-based Simulation (Horiguchi et al., 2007)
Explicit representation of conceptual
knowledge
Inference of behaviour
from structure
Qualitativeness
(Intuitive representation of
model)
Reductionist
(Compositional modelling)
Explaining the behaviour in terms of their cause-effect
chain
8
1.2.2 Qualitative Process Theory (QPT)
Ontology is a specification of a representational vocabulary for a shared domain of
discourse. In the simplest sense, it is a model of “meanings” that represents our
conceptualization of the world. Ontology has the potential to facilitate the formation of
semantic relationships between various portions of useful information to enhance the
learning experience in an educational setting (Yang, 2007). Our qualitative models are
constructed using QPT. QPT is a process-centred ontology that supports knowledge
acquisition (gather the relevant knowledge) and model construction (creation of
relationships among chemical parameters) in the simulation environment. Figure 1.3
shows a general view of QPT.
A physical situation is usually described in terms of a collection of objects, their
properties and the relationships between them. QPT provides the means to draw types
of basic, qualitative deduction and reasoning about the combined effects of several
processes in a physical situation. The modelling constructs of QPT will be discussed in
turn. Note that words typed in italics which will appear in later illustrations are QPT
modelling constructs. In QPT, a model can be constructed for an individual view or a
process. The individual views describe objects and their general characteristics while
processes are the agents that cause changes in objects over time. In other words, a
process supports changes in system behaviour. There are five slots in a process
specification, namely Individuals, Preconditions, Quantity-conditions, Relations
(statements about functional dependencies among objects’ characteristics) and Direct
Influences (denoted by I+ or I−).
One of the important modelling constructs for describing the relationships between
quantities is the qualitative proportionalities (the P+/P-). These constructs propagate
9
the effects of processes that express unknown monotonic functions
(increasing/decreasing/unchanged) between two quantities (e.g. charge, covalent bond,
lone pair electrons, electro-negativity and nucleophilic reactivity). A quantity space is
defined by a set of alternating points (e.g. [negative, neutral, positive]). For example, at
any given point of time, the charge of any atom is either negative or neutral or positive
(Figure 1.4). The signs of change (e.g. [-1, 0, 1]) are used for assigning values to
quantities. Note that “-1” means decreasing (i.e. the value on the left side of the current
state in a quantity space will be assigned); “0” is non-changing, and “1” means
increasing (i.e. the value on the right side of the current state in a quantity space will be
assigned). When a quantity’s value is above or below a specific limit point, some
physical phenomena occur. As an example, suppose that an atom’s current charge is
“neutral” and the sign of change for this parameter in a given process is “1”, then the
new state of its charge will be “positive”. Direct influences are represented as I+ (Q1,
Q2) and I- (Q1, Q2) where “Q” stands for “Quantity”. Influences can either be positive
or negative. A subset of the QPT notational system used in this work is defined Table
1.1. In chemistry, changes are caused by continuous physical processes. These changes
propagate through the system via qualitative proportionalities which indicate causal
relationships between quantities.
10
Figure 1.3 A general view of QPT.
charge = [negative, neutral, positive]
quantity quantity space
Figure 1.4 A “charge” quantity and its space.
Individual-Views modelling
(Describe objects and their general characteristics)
Processes modelling
(Processes are agents that cause changes in objects over
time) Qualitative Reasoning Ontology
Key ideas:
1. Direct influences 2. Qualitative proportionalities 3. Sign of change 4. Amount in magnitude 5. Correspondences 6. Quantity space
Interacting
Processes
(All causal changes stem from physical processes)
11
Table 1.1: Some notations and semantics of the QPT modelling constructs.
Notation Description
Is Direct influence notation. E.g. s = {-1, +1}, where +1 = increase (more covalent bonds are made); -1 = decrease (reduce a covalent bond). Only processes can have direct influence.
P
Proportionality statements. E.g. Q1 +
−P Q2 means “an increase in Q2 will cause a decrease in Q1”, where Q1 and Q2 are chemical parameters.
Am Amount in magnitude. E.g. the number of valence electrons of an atom.
Ds Sign of change. E.g. -1 = negative (move to the left side of the
quantity space), 1 = positive (move to the right side of the quantity space). A quantity space is a set of candidate values for a chemical parameter used in simulation.
This work expresses the general chemical principles of organic reactions as QPT
processes, where processes are the mechanisms that support changes in the chemical
system behaviours. The five slots of a QPT process specification are depicted in Figure
1.5.
QPT process
3. Entry-condition
4. Direct-influence
5. Relations
1. Individuals 2. Preconditions
Figure 1.5 The five slots of a QPT process.
This ontology is suitable for testing our reaction cases since in this formalism, changes
are caused by continuous physical processes (e.g. the series of organic processes),
which provide the notion of mechanism for causality (the way a phenomenon or a
12
prediction is explained). In QPT, histories are used to represent how things change
through time. This notion of history provides the means to describe the mechanism
used to produce a synthesis path (the path that leads the initial substrate to the formation
of the final product). We will also demonstrate that the modelling constructs presented
in Table 1.1 are sufficient to provide explanation at a conceptual and intuitive level to
chemistry students. Subsequently, we use representations inspired by QPT to develop
the reasoning algorithm for the simulator prototype which will be discussed in detail in
Chapters 3, 4 and 5.
1.2.3 Organic Reaction and Organic Mechanism
Reactions: An organic reaction is a chemical reaction involving organic compounds,
usually between an electrophilic centre and a nucleophilic centre. In any chemical
reaction, some bonds are broken and new bonds are made. A bond is what links two
atoms together within a structure. It is formed by the sharing of a pair of electrons
between two atoms. Atoms can form bonds by sharing unpaired electrons (also called
“lone pair electrons”). Often, these changes are too complicated to happen in one
simple stage. Thus, usually a reaction may involve a series of small changes one after
the other. A reaction mechanism describes this series of changes. Reactions can be
classified as acid/base reactions, functional group transformations (one functional group
can be converted into another) or as carbon-carbon bond formations. Reactions can also
be classified according to the process or mechanism taking place and these are specific
for particular functional groups (Groutas, 2000; Atkins and Carey, 1997).
Mechanisms: A detailed description of how a reaction can occur is called “reaction
mechanism” (Fessenden and Fessenden, 1998). A reaction mechanism (may also be
13
called “organic mechanism” or simply “mechanism”) can be defined as “a description
of the sequence of steps that occur during the conversion of reactants to product”. The
mechanism tells us how bonds are formed and broken and in what order things happen.
In other words, it is a structural description of the individual reaction steps during
conversion. Mechanism of reactions shows that chemical reactions occur by specific
routes. Hence, the route can be used to justify results and to explain why the reactions
perform the way they do. This is because the “how” of a chemical reaction is the issue
to be explained when a mechanism is proposed for it. The above description is rather
symbolic and qualitative, not needing quantitative data to predict the final products, or
explain towards the simulated results.
Organic synthesis, on the other hand, is the study of creating new compounds and the
planning for the “creation” task would require understanding of organic mechanisms.
Often, organic chemists will identify the electron-poor site and electron-rich group
when trying to work out a reaction mechanism. Most of the time, organic chemists
could work out the mechanisms by using commonsense developed from their chemical
intuition and knowledge. As one can see, the nature of the problem is “qualitative” in
that it is about electron movement, for example, from where should one start moving
the electrons around and to where the electrons should go and why so – a very suitable
field for applying the qualitative reasoning approach.
An organic chemist will usually look into the reaction mechanism to help explain the
outcome of a reaction. When chemists want to create a novel compound, they would
first draw the reactant structures and then draw the structure of the product(s). With
their chemistry knowledge and chemical insights, they then work out possible
mechanisms from reactant to product. In this scenario, the chemists will attempt to
14
carry out organic synthesis by following the mechanisms they proposed. Examples of
reaction mechanism are SN1 (unimolecular nucleophilic substitution), SN2 (bimolecular
nucleophilic substitution) and elimination.
Families of organic compounds are characterized by the presence of distinctive
functional groups. A vast majority of organic reactions take place at functional groups.
Functional groups are the structural units responsible for a given molecule’s chemical
reactivity. A functional group is a portion of an organic molecule, other than carbon
and hydrogen (the normal hydrocarbon framework) or which contain bonds other than
C−C and C−H. In this approach, each organic reaction is described as changes made
on the chemical parameters (e.g. charge, covalent bond and lone pair electrons) of the
functional groups. These units will determine what type of chemical process can be
activated. In the scope of this work, two specific functional groups, namely “OH” and
halogen atoms were tested. The mechanisms used for reactions described in this thesis
are SN1 and SN2 involving the two functional groups given earlier.
Many chemistry students learn organic reactions by memorizing the steps and formulas
of each reaction which can easily be forgotten. They face difficulties in dealing with
the principles governing the processes and the cause-effect interaction (the causal
theories) among these processes. Traditional approaches to organic chemistry
modelling are based on formulas and quantitative data. These approaches do not make
good use of the qualitative nature of organic mechanism knowledge. The lack of tight
coupling between concepts and their embodiment makes most education software
unable to explain or justify its results. Meanwhile in chemistry lab, students only see
the results of a reaction which may take the form of either some gases being released or
colour changes occurring in the reaction. Without proper explanation, these
15
observations do not help much in nurturing their understanding of the subject. Learning
organic reaction mechanism needs some basic skills and these skills are related to the
nature of the problems. Students’ barriers to understanding the course is depicted in
Figure 1.6.
Seen as a
difficult
subject Students learn the
subject by
memorizing the
reaction steps
In lab,
experiments
cannot explain
results
Requires
good chemical
intuition
Needs to
know the
principles that
govern the
processes
In classroom, pens & chalks
are used to show the
students how to use arrows
to indicate movement of
electrons
Understanding
organic reaction
and organic
mechanism
Figure 1.6 Students’ barriers to understanding the organic chemistry course.
Many AI techniques have been used to develop software for organic synthesis and the
study of reaction mechanisms. These applications do not utilize qualitative reasoning
approach, and they are not QPT-based systems. Previously, simulation of chemical
reactions relied heavily on precoded facts and rules where knowledge is first sought
from chemists and then transferred it into computer representation. The process is time
consuming and always causes bottlenecks in information upgrading (refer to Chapter 2
for further details). On the other hand, qualitative reasoning provides an alternative
way for chemist to represent, develop, organize, and implement models. Advantages of
this approach include the possibility of deriving conclusions about the organic
chemistry phenomena without numeric data; a compositional approach that enables the
16
reusability of models representing partial behaviours (such as a small step in the entire
reaction route) and the capability to provide causal interpretation of system behaviour.
We will now give one example of an organic chemical reaction to show that qualitative
description is sufficient for understanding its underlying chemical principles. In the
example, quantitative data and precise measurements are not at all required. In
chemistry class, students are taught that the compound “(CH3)3C−OH2+” will undergo a
“break-bond” process. The cleavage of the carbon-oxygen bond in tert-butyloxonium
ion ((CH3)3C−−−−OH2+) is due to the unstableness of the oxygen atom since it now has
three covalent bonds (valency for oxygen is two). Once the carbon-oxygen bond is
broken, the oxygen will regain its stability. However, the charge on carbon in the main
chain of the organic compound will become positive since one of its valence electron is
donated to the oxygen in order to neutralize it. The tertiary carbocation ((CH3)3C+) is
now unstable and it is reactive. Carbocations are transient, electron-poor and highly
reactive species. The changes that propagate from a chemical parameter to another can
be easily represented and modelled as a few functional dependency statements (or
“qualitative proportionalities” in QPT term), as follows. Note that “Y +
−P X” means
“increasing X followed by decreasing Y”, and those words after the “//” sign are
remarks. The initial values that are assigned to the parameters can be taken from the
basic chemical facts knowledge base.
lone-pair-electron(O) −
+P no-of-bond(O)
// decreasing oxygen’s covalent bond will increase its lone-pair electron
charge(O) +
−P lone-pair-electron(O)
// increasing oxygen’s lone-pair will decrease the charge on it; oxygen is being neutralized
charge(C) −
+P no-of-bond(C)
// decreasing carbon’s covalent bond will increase its charge; carbon is now positive
17
Overall, organic reactions are much easier to classify into generic structural types than
inorganic reaction (the specific reasons are discussed in Chapter 2). However, in order
to characterize and reason with instances of these structural types, appropriate
qualitative representations are needed. It is a challenge indeed to cast expert knowledge
into QPT models representing the behaviour of organic reactions and even more
challenging when the chemical processes are to be made reusable to fulfil one of the
research objectives as given in Section 1.4.
1.3 Problem Statement
When students were asked whether they find learning organic reaction difficult, most of
them claimed it is so. Students get confused mainly due to the abstract nature of the
problem. As such, most of the students learn organic reaction by memorizing the steps
involved in a reaction, and the formulas taught in classes. Consequently, some students,
particularly weak learners would require additional learning aids such as a software tool
to assist them with their learning. If students learn the subject by memorizing the steps
and format/pattern of each reaction, then they may not be able to answer simple
questions such as: Why would this reaction go this way? What is favourable about this
particular step? What is causing the reaction to begin? Why was the process stopped?
What happened to the nucleophile and electrophile? This is the educational problem
that is being solved, as memorizing formulas is not a good method in any type of
learning. In science education, it is believed that students should understand the
qualitative principles that govern the subject including the cause-effect relationships in
processes before they are immersed in complex problem solving. When these
fundamental skills are acquired, the entire learning activity can be made more effective.
The nature of the chemistry domain described in this work is very qualitative (Tang and
18
Syed Mustapha, 2006) and understanding the subject would require application of
chemical insight and good use of chemical commonsense. The models people use in
reasoning about physical world are called mental models (Gentner and Stevens, 1983).
It is useful to study the connection between mental models of chemists when solving the
reaction problems and qualitative reasoning approach. The result of the study can help
represent domain knowledge in the modelling constructs of the QPT ontology.
Since no work has been done on the application of qualitative reasoning for modelling
organic chemical reactions, along with an explanation facility for describing those
reactions, it is aimed to determine to what extent qualitative reasoning could be useful
for predicting the final products of an organic reaction and explaining its simulation
results.
Automating the construction of QPT process model during runtime is also a work to be
done since the so-called “model automation” is still an issue in the development of QR
related tools. In particular we would like to seek an answer for the question “can the
qualitative models be automated in this domain?” Furthermore, the extent to which
chemical process generalization can be accomplished to support different types of
reaction mechanism is also of our interest.
Computers have always been regarded as powerful tools for educational purposes.
Traditional ways of using computers in educational settings are often limited to
supporting the creation, sharing and presentation of information. The traditional
approach for developing science educational software has shortcomings, in that the
conceptual understanding can only be found in the program’s documentation and not in
the software itself. The lack of tight coupling between concepts and their embodiment
19
makes it difficult to explain the results (Forbus, 2001). These programs cannot
“explain” because the results are obtained through chaining the rules during runtime or
by searching the reaction routes that have been precoded. As such, traditional
chemistry educational software is inadequate to promote understanding in chemistry
subjects as the programs using traditional approach will only return the result. We see
there is a need to find new approaches to develop software that can help explain
chemistry phenomena to chemistry students. The qualitative reasoning approach
addresses this issue to a promising extent (Bredeweg and Struss, 2003).
QALSIC (Pang et al., 2001; Syed Mustapha et al., 2002; Syed Mustapha et al., 2005), a
previous work that uses QPT for modelling inorganic chemistry reactions faced several
problems. One of the major problems is the incorrect behaviour prediction of the
chemical system. The QALSIC program has not accomplished three major tasks: (1)
The modelling phase is not automated, (2) reusable processes are not clearly
demonstrated, and (3) evaluation of the software and mental change survey were not
conducted. Moreover, most of the explanations are precoded. A complete review of
QALSIC’s limitation is given in Chapter 2.
1.4 Objectives
There are three main objectives of this work. The primary objective of this study is to
design a qualitative reasoning framework that can be used to qualitatively model,
simulate and explain organic reaction mechanism for learning purposes. Due to the
wide scope of qualitative reasoning (from task-level reasoning, ontologies, techniques,
cognitive modelling, application, to creating new kinds of educational system called
articulate software), in the design of the framework, we focused on a few issues relating
20
to: (1) Improvement of the explanation generation approach since current chemistry
software cannot appropriately explain a chemical phenomenon, (2) Design of the
qualitative models from the perspective of promoting “model reuse” in order to support
the simulation of multiple organic chemistry reactions, and (3) Automation of the QPT
model construction for organic processes once the user has entered a pair of reactants.
The secondary objective is to develop a software tool (simulator prototype) to study the
extent to which the explanation generation approach can help improve a student’s
understanding of the subject. The tool should be able to accomplish the following
tasks: (1) To make correct predictions for a large set of reacting species, with no
specific answers in the knowledge base; purely through reasoning from the fundamental
principles of organic reactions, and (2) To improve students’ reasoning ability and their
understanding of the organic chemistry subject when they are exposed to the tool. We
believe that using a software tool installed on the student’s machine can provide
valuable advantage in education. Using computers to complement human instructors
have also been a long-standing motivation for research on AI in education.
The third objective is to investigate the main problem faced by QALSIC. The
investigation serves to solicit the main reason as to why the software sometimes returns
incorrect answers.
1.5 Research Questions
Reflecting on the current state of the art in chemistry educational software, what is still
missing is the application of qualitative reasoning to solve organic chemistry problems
and software that can explain its results. The overall research goal of this work is to
investigate how the qualitative reasoning approach can be utilized for this purpose and
21
to develop a framework for the simulation of organic chemistry reactions using the
approach. The framework will be implemented in a simulator prototype called QRiOM
(Qualitative Reasoning in Organic Mechanism) in order to test the simulation and
explanation capabilites. Briefly, the software is developed to play two roles; to
substantiate the achievement of the objectives and to support student learning in the
organic chemistry course. To accomplish the work, the research questions are set as
follows:
• How do chemistry experts construct their mental models when solving problems?
• How can QPT be used to represent the domain knowledge?
• How can qualitative reasoning be used to support organic reaction simulation?
• How can qualitative reasoning be used to support learning of organic processes?
• How can the modelling constructs of QPT be used to explain a chemical
phenomenon?
• How can qualitative model construction be automated?
• How can the qualitative model be made reusable?
• How can causal graph generation be automated at runtime?
• How can knowledge validation be carried out to ensure correct use of domain
knowledge in a simulation?
• How effective and useful is QRiOM as viewed by the users?
• How can a student’s mental change be measured?
• How can QALSIC be tested to reveal its prediction deficiency?
The above questions will be addressed in this work by a combination of theoretical
analysis, algorithm development, rapid prototyping, and user evaluation. The main
educational goals of the qualitative simulator are that
22
a) The students’ conceptual understanding of the subject is improved
b) After using the software, the students are able to explain a chemical phenomenon
in a more elaborate way as a result of acquiring skills in knowledge articulation
c) The students will undergo mental change such that they gain more confidence in
solving new problems
It is important to study the connection between mental models and the qualitative
reasoning approach. When the mental models of chemistry experts can be captured and
represented in QPT, then, weak students can be assisted in solving the same problem.
As a result, the students’ reasoning ability and their logical thinking will be improved
when learning from the software, especially from the causal explanation that explains a
situation using only ontological primitives of QPT. Moreover, conducting experiments
could be expensive and hazardous, but the software simulation approach allows users to
repeat an experiment any number of times at no extra cost.
In order to achieve all the objectives, the following tasks are needed:
• Capturing human expertise in the field of organic reaction mechanisms and
representing them as qualitative data in the form of qualitative models.
• Using a process-based ontology (the QPT) to represent chemical knowledge
qualitatively.
• Designing qualitative reasoning algorithm for reaction mechanism simulation.
• Finding an easy way of generating explanation effectively in order to facilitate
mastering of organic reaction concepts via the QPT-based explanation.
• Developing an algorithm that enables model automation.
23
• Classifying chemical processes for a variety of organic substrates in order to
promote model reusability.
• Developing a framework for hierarchical structuring of processes to facilitate
effective use of knowledge. This is not found in QALSIC.
• Developing a small set of chemistry ontology called OntoRM (Ontology for
Reaction Mechanism) for use with reaction mechanisms. The ontology will be
used to perform validation during reasoning.
• Developing and implementing a simulator prototype QRiOM.
• Evaluating the effectiveness of QRiOM.
• Studying how learners gain conceptual understanding when interacting with
QRiOM.
• Investigating the QALSIC software by testing it with a mixture of inorganic
reaction experiments with an intention to seek the main cause for the system error
(e.g. not producing correct prediction).
Table 1.2 tabulates the relationship between the research problems and questions, the
objectives, and the thesis chapters that fulfilled them.
24
Table 1.2: Relationships between research problems and questions, objectives and the corresponding thesis chapters that answered them.
Research problems Research questions Objectives Related
chapter
Organic reaction
mechanism is a difficult
subject to learn, even at
the conceptual level of
understanding. a. Most of the students
learn organic chemical reactions by memorizing the steps involved in a reaction
b. No work has been done on solving organic reaction problem using qualitative reasoning approach
• How do chemistry experts construct their mental model when solving the problem?
• How can qualitative reasoning be used to support the learning of organic processes?
• How can QPT be used to represent the domain knowledge?
• How can qualitative model construction be automated?
• How can the qualitative models be made reusable?
• How can qualitative reasoning be used to support organic reaction simulation?
• To capture human expertise in the field of organic reaction mechanisms and represent them as qualitative data in the form of qualitative models.
• To examine and use a process-based ontology (the QPT) to represent chemical knowledge qualitatively in order to model the behaviour of organic reaction mechanisms.
• To develop algorithm that enables model automation.
• To classify chemical processes for a variety of organic substrates in order to promote model reusability.
• To design qualitative reasoning algorithm for reaction mechanism simulation.
Chapter 3
Chapter 4
Existing chemistry
educational software
cannot explain simulated
results.
a. Traditional chemistry
educational software is inadequate to promote understanding
b. There is a need to find new approaches to develop software that can help explain chemistry phenomena
• How can the modelling constructs of QPT be used to explain a chemical phenomenon?
• How to automate causal graph generation at runtime?
• How can the domain knowledge (represented in QPT), and OntoRM ontology be effectively used?
• How can knowledge validation be carried out?
• How effective is the simulator as viewed by the users?
• How can a student’s mental change be measured?
• To find an easy way of generating explanation effectively in order to facilitate mastering of organic reaction concept via the QPT-based explanation.
• To automate causal graph (state graph) generation as a mean to explain an organic process phenomenon.
• To develop a reasoning framework for organic reaction simulation and explanation.
• To develop a small set of chemistry ontology called OntoRM for use with reaction mechanisms for knowledge validation use.
• To define the types and roles of chemical knowledge at different abstraction lelvels in order to facilitate effective use of the knowledge. This is not found (or rather unclear) in QALSIC.
• To develop and implement a simulator prototype QRiOM.
• To evaluate the effectiveness of QRiOM and its explanation facility.
• To measure a learner’s mental change when interacting with the software.
Chapter 4
Chapter 5
Chapter 5
Chapter 6
QALSIC – a qualitative
simulator for modelling
inorganic chemistry
reactions faced several
problems.
• How can QALSIC be tested to reveal its prediction deficiency?
• To test QALSIC software with a mixture of inorganic reaction experiments with an intention to seek the main cause in the system error (e.g. producing incorrect prediction).
Chapter 2
25
1.6 Scope of Research
In this work, qualitative reasoning based on qualitative process theory ontology is used
to simulate nucleophilic substitution reaction specifically on the following two organic
mechanisms:
• Unimolecular nucleophilic substitution (SN1)
• Bimolecular nucleophilic substitution (SN2)
1.6.1 System Scope
A simulator prototype, named QRiOM is developed. Features of QRiOM are
summarized as below:
• The system can only accept and recognize organic compounds as substrates. The
substrates are limited to alkanes, alcohols and alkyl halides.
• The system can model the behaviour of organic reaction automatically based on a
<substrate, reagent> input pair.
• The system can predict and return the final products based on qualitative
reasoning approach.
• The system is able to recommend an organic mechanism for a given pair of
reactants.
• The system can generate various forms of explanation (texts and diagrams) based
on the modelling constructs of QPT.
26
1.6.2 Course Scope
The software is suitable for use in the following courses:
• “Introduction to organic chemistry” at undergraduate level.
• “Organic reaction mechanism” at undergraduate level.
1.7 Main Results
A qualitative reasoning framework that supports model construction, model reasoning,
results prediction and justification has been developed. The framework has also been
implemented, resulted in QRiOM, a simulator prototype that can simulate and explain a
number of organic reactions to the chemistry students. QRiOM is the first chemistry
learning software that uses qualitative reasoning to explain chemical phenomena of
organic reactions. In particular, this work describes the use of QPT ontology to model
the conceptual knowledge and chemical theories of organic reactions and reaction
mechanisms at the finest granularity of processes, such that explanation at deeper level
can be achieved. Qualitative reasoning based on QPT models is able to predict the final
products (outcomes) of a reaction and to explain the predicted outcomes. Besides, the
qualitative models in the reasoning framework can support many types of reaction
mechanisms (other than the nucleophilic substitution reaction defined in this work).
Upon completion of the QRiOM software development, a preliminary system
evaluation was carried out. The results of the initial evaluation of QRiOM showed that
it is effective in terms of its ability to promote understanding of organic reactions
through the inspection of the explanation generated by the software.
In summary, the main results in the design and development of the reasoning
framework and the evaluation of QRiOM are as follows:
27
• A framework for modelling and simulation of organic reactions has been developed.
• Model automation logic has been formulated.
o Automating the construction of QPT models is made possible by first
identifying the type of the reacting species, then the chemical process that can
occur.
• Model reuse is supported by the framework.
• A simulator prototype for explaining organic reaction to the chemistry students has
been developed and implemented.
• OntoRM has been designed for use in validating the knowledge used in reaction
mechanism simulation, as well as the simulated results.
• An analysis of application of QR approach in inorganic versus organic reaction
simulation was carried out.
o Organic chemistry reactions are relatively easier to be modelled using QR
approaches as compared to inorganic chemistry reactions.
• User evaluation of the tool was conducted where a positive response was received
as far as student evaluation is concerned.
1.8 Thesis Structure
This thesis consists of seven chapters. Chapter 2 is a review of the relevant literature.
The review concentrates on early qualitative reasoning applications and systems that are
developed using the qualitative process theory technique. Next, the chapter presents the
chemists’ way of solving organic chemistry problems. Then a review of computer-
assisted applications in organic chemistry is given. This chapter also presents two
representation schemes for organic molecules (SMILES and InChi). A review of the
literature on two related works (LHASA and QALSIC) is then presented. Finally, a
28
brief description of the strengths and weaknesses of the reviewed approaches and
systems is presented.
Chapter 3 provides an overall description and justification of our model construction
logic. A review of existing literature specifically focuses on qualitative modelling is
first provided followed by performing a study on chemical reactions involving alcohols
and alkyl halides. From the study, “make-bond” and “break-bond” were identified as
the generic processes in the simulation of organic reactions involving the two groups of
substrates. From the analysis of various chemical reactions occurring under SN1 and
SN2 mechanisms, the common set of chemical theories and behaviour have been
identified for the two processes; from which the model automation procedures are
formulated. Proofs are given to justify the model automation procedures. Next, the
mapping of chemical theories onto QPT constructs is discussed. This chapter ends with
several examples of learning activity that are derived from the inspection of qualitative
models.
Chapter 4 describes the qualitative reasoning scenario for numerous organic reaction
problems. A review of existing literature specifically for qualitative simulation and
explanation is first provided, followed by a detailed discussion on the simulation and
explanation generation techniques. This chapter underpins how the QR approach can
be used to support a learning task, and that students actually manifest the kinds of
learning behaviour we anticipated. Reusability of processes is also described in this
chapter while the entire reasoning framework is left for the next chapter of this thesis.
Chapter 5 describes the QR framework developed in this research and discusses the
simulation results. This chapter discusses the roles of each functional component in the
29
framework. The chapter starts with a schematic view of the framework. Then it
describes the workflow of the framework. The design logic for each component is also
presented. This chapter also outlines all the algorithms used in each main component of
the framework. Reusability of the framework components is duly described in this
chapter. The OntoRM ontology for reaction mechanism is also presented. A few
validation examples to show how OntoRM helps validate (and constrain) the use of the
chemical knowledge are also included. The idea and motivation of knowledge
structuring are also presented. Finally, the simulated results are presented and
discussed.
Chapter 6 presents the evaluation results of the simulator prototype. The design of the
evaluation process places particular emphasis on how the explanation can help enhance
students’ understanding of organic reactions and the reaction mechanisms used in the
simulation.
In Chapter 7, the thesis is concluded by presenting the main results and achievements of
this work. Some of its limitations are described, and suggestions for future research are
also provided.
Figure 1.7 summarizes the thesis layout in graphical form.
30
Figure 1.7 Thesis layout.
Chapter 1 Introduction
Chapter 2 Literature Review
Chapter 3 Qualitative Modelling of
Organic Reactions
Chapter 5 Qualitative Reasoning Framework for Organic
Reaction Simulation
Chapter 4 Qualitative Simulation and
Explanation Generation
Chapter 6 Evaluation of QRiOM
Chapter 7 Conclusion
31
Chapter 2 Literature Review
2.1 Introduction
In this chapter, the state of the art of qualitative reasoning applications is reviewed, with
special attention paid to application of QPT to building educational software for
teaching and learning purposes. There is a wealth of literature on the topic of
qualitative reasoning systems in generating explanation. However, very few of this
literature are directly related to our study domain. Instead, as will be seen through this
review, the majority of the studies discuss the modelling activity as the way to acquire
knowledge and the application domains are not organic reaction mechanism. The
structure of this chapter is as follows. Section 2.2 reviews the relevant literature on
qualitative reasoning applications. Section 2.3 reviews the literature on previous work
using qualitative process theory. In Section 2.4, the suitability of the selected domain is
discussed. In Section 2.5, computer-assisted applications and use of AI in chemistry are
presented. Section 2.6 discusses the SMILES and InChI standards for complex
encoding organic molecule, a popular input format for complex organic compound. A
comprehensive study of related works is given in Section 2.7. Section 2.8 concludes
this chapter.
2.2 Review of the Literature on Qualitative Reasoning Applications
As development in qualitative reasoning has direct influence on educational software
development, some representatives of QR related tools (software systems) are reviewed.
The tools are divided into two categories, namely for engineering and education.
32
2.2.1 In Industry
Earlier discussion on QR applications is mainly focused on physics and engineering.
One of the discussions is on applying qualitative reasoning for complex controllers
(Bratko and Šuc, 2002; Bratko and Šuc, 2003a). The work explored qualitative data
mining to find qualitative patterns from numerical data. Some of their works include (1)
behavioural cloning (2) reverse engineering of controllers (3) QUINN induction
program for machine learning of qualitative trees (Bratko and Šuc, 2003b).
Application of QR and model-based technology in the automotive industry to support
on-board diagnosis, failure analysis, and automation of electrical design analysis tasks
are among research conducted by Advanced Reasoning Group at The University of
Wales, Aberystwyth (Advanced Reasoning Group Homepage,
http://www.aber.ac.uk/compsci/Research/mbsg/). Many prototypes and products have
been developed by the group. Some of these are SoftFMEA, Dougal and AutoSteve
that provide FMEA needed by some of the diagnosis carried out on the VMBD project
that runs on model-based on-board demonstrator vehicle. Some of the solutions (e.g.
the industry processes) have been deployed by car manufacturers (Struss and Price,
2004). Application of QR to spatial data mining in pandemic disease outbreaks and
spatial reasoning charts another milestone for the practical value of QR. For example,
Bailey et al. (2006) reported how analysis of spatial datasets can be done through
model-based reasoning for more effective use of data. Bailey and Zhao (2003) also
described approaches to data-poor and data-rich problems in Qualitative Spatial
Reasoning. More recent applications are the use of QR in learning turn-based strategy
game (Hinrichs et al., 2006), where qualitative models are used for providing and
acquiring strategies for an unsupervised player. Ricardo et al. (2006) applied QR to
manage Information Technology (IT) services such as capacity and incident
management when accessing the Web/Application/Database servers via Web pages.
33
The causal relations found in qualitative models was said to be of importance for
understanding IT system in improving Web services.
2.2.2 In Education
Education is a popular application area in qualitative reasoning research. The potential
of this new methodology for building science educational software has been
demonstrated by several high cited works such as CyclePad (Forbus, et al., 1999),
VisiGarp (Bredeweg and Winkels, 1998; Bouwer and Bredeweg, 2001; Bouwer, 2005),
ALI (D’Souza et al., 2001), and Betty’s Brain (Biswas et al., 2001). These systems
posses a common feature, that is the ability to predict and explain the behaviour of
physical systems in qualitative terms in an educational and training setting. The success
of the software to promote and induce learning and the birth of articulate software
(Forbus, 1997; Forbus and Whalley, 1994) marked another milestone for further
investigation, application, and popularity of qualitative reasoning techniques.
Modelling provides a means for articulating knowledge (Bredeweg and Forbus, 2003).
Describing physical processes in qualitative (conceptual) terms, and building a model
using a suitable QR ontology will require the learner to acquire reasonable concepts
(e.g. be able to articulate various knowledge aspects) about the subject. There are
different ways in which learners can acquire knowledge. Inspecting ready-made
simulations is one; another approach is to engage learners in building models as a way
to acquire knowledge. Typical examples are VModel (Forbus et al., 2001), Betty's
Brain (Biswas et al., 2001) and VisiGarp which became part of the Garp3 (Bredeweg et.
al, 2007) which is an environment that can be used to build models but also to learn
34
from running and inspecting ready-made models. Both the VModel and VisiGarp
environments use diagrammatic representations to facilitate knowledge articulation.
Over the past 20 years many prototypes and full systems using the QR approach have
been developed. Besides the five systems (CyclePad, VisiGarp, ALI, VModel, and
Betty’s Brain) stated above, other QR related tools for educational purposes, but not
limited to, are as follows:
• High school level mathematics by Neuper and Wotawa (2002). It is a framework
for handling knowledge based on model-based reasoning. The work constructs
mathematical model from textual description to describe a “mathematical concept”.
Generation of explanation is only possible in the modelling phase. Techniques
used are script and rewriting formula by application of theorems.
• CPRODS (Sime, 2002). The work consists of six qualitative and quantitative
models. Instructional design is provided. An overview of hypothesis scratchpad is
included. However, there is no reflection on the learning process. As a result, it
cannot differentiate between an excellent teacher and a poor teacher.
• A cognitive tool called MMforTED (Toppano, 2002). Implemented as hypermedia,
constituted by a collection of cases of simple electrical and fluid mechanical
devices. The tool consists of graphs of models that can be used for problem solving
or communication, and being able to reason about domain concepts and their
relations. The use of the Web as a content provider as well as the delivery medium
of instruction is considered pioneering at that time.
• Learn C++ tutoring system (Kumar, 2002). The tutoring system supports program
animation.
• Intelligent Tutoring Systems for Training by Vadillo and Ilarraza (1995). The
simulation run by the system is based on components ontology and QPT.
35
Structured behavioural explanations can be generated based on a causal domain
representation.
• QALSIC is a system for inorganic chemistry analysis and simulation using process-
based simulation approach. More discussion on this work is presented in Section
2.7.2.
• Salles and Bredeweg (2002) and Salles et al. (2003) explored qualitative models in
ecology and their use in intelligent tutoring system. The goal is to model the
effects of fire on vegetation dynamics for educational purposes. QR approaches
used are modelling based on SIMAO (Guerrin, 1991; Guerrin, 1992) and QPT-
based modelling.
• Authoring Graph of Microworld (Horiguchi and Hirashima, 2005; Horiguchi and
Hirashima, 2008; Horiguchi and Hirashima, 2009). It is a method that can assist an
author in indexing a set of microworlds based on the constructed qualitative
models. By using Graph of Microworld, it is possible to adaptively select the
microworld a student should learn next.
• Error-Based Simulation (ESB) applied QSIM as the qualitative reasoning technique
to predict qualitative behaviour in mechanics problems and to generate feedback
for learning from mistake (Hirashima et al., 1998; Hirashima and Horiguchi, 2001;
Horiguchi and Hirashima, 2006). Hirashima’s group has developed a prototype
and conducted an evaluation to measure what conceptual changes are caused by
using the EBS approach. Results showed that EBS learning was useful (Horiguchi
et al., 2005; Horiguchi et al., 2007).
A summary of the reviewed literature on QR work (and systems) is provided in
Appendix A. Most of the QR work has side products which are Intelligent Tutoring
Systems (ITS). Our work is not to build an ITS, but to prove that our reasoning
36
framework is feasible and practical and when implemented, the simulation results and
explanation generated by the software can help improve the chemistry students’
understanding of the organic reaction processes. Traditionally, simulations are based on
mathematical models which have several shortcomings when it comes to explaining
them to relative novices and since our target users are students, we believe qualitative
explanation will be more appropriate and useful. Model construction activity is not
included in our implementation. This is because it does not suit the learners’
background of this work. Instead, qualitative modelling is automated for their
inspection.
2.3 Review of the Literature on Work Using Qualitative Process Theory
Representatives of application of qualitative reasoning based on QPT are as follows:
• Ecology simulation (Salles et al., 1996; Bredeweg et al., 2006). The simulator is
able to predict and explain the behaviour of physical systems in qualitative terms.
• GARP (Generic Architecture for Reasoning about Physics) by Bredeweg (1992).
This qualitative reasoning engine is implemented in SWI-Prolog that allows users
to simulate qualitative models.
• HOMER by Machado and Bredeweg (2002). In HOMER, concepts and their
relationships are represented graphically. The system included the design of a
support module that can guide the users through the model building process. A
causal model viewer is also included.
• VisiGarp by Bouwer and Bredeweg (2001). The system implements a graphical
interface to GARP which allows users to inspect qualitative simulation models by
interacting with automatically generated visualizations.
37
• WiziGarp by Bouwer (2005). It is a prototype that extended the functionalities of
VisiGarp by utilizing aggregation techniques to simplify qualitative simulations
and by incorporating diagrams and textual means of communication.
• VModel (Forbus et al., 2001). The approach to modeling is to create a student-
friendly visual notation for qualitative process theory (Forbus 1984) and create a
software environment that helps students express their qualitative, conceptual
models as an aid to learning. The VModel qualitative modeling framework is
richer, incorporating physical processes and a student extendable ontology of types
of entities.
• QCM (Dehghani and Forbus, 2009) is a successor to VModel. QCM provides the
basic functionality needed for cognitive scientists to build, simulate and explore
qualitative mental models. QCM is the first modelling tool which has been
specifically designed for cognitive scientists. QCM provides a framework in which
the agent’s knowledge about the causal structure of the world can be captured using
the QPT formalism while the agent’s uncertain knowledge and expectations about
the outcomes of his/her actions can be captured by subjective probabilities and
represented by a Bayesian Network. Modellers can switch the mode of reasoning
from QPT to Bayesian and make probabilistic models. This feature allows
cognitive scientists to take advantage of different types of reasoning available in
both formalisms.
• CyclePad (Forbus et al., 1998) functions as a computer-aided design (CAD) system
for the conceptual design of thermodynamic cycles. Technologies used in CyclePad
are: Constraint propagation, logic-based truth maintenance, qualitative
representations, and compositional modelling (Forbus and Whalley, 1994; Forbus
et al., 1998; Forbus et al., 1999; Forbus et al., 2001). Explanations in CyclePad are
38
represented by structured explanations, an abstraction layer between the reasoning
system and the interface.
• QALSIC (Pang et al., 2001; Syed Mustapha et al., 2002; Syed Mustapha et al.,
2005). A qualitative simulator for learning inorganic chemistry. The limitations of
this software tool will be discussed in Section 2.7.2.
The last two applications of QPT provide the motivation for us to conduct the research
described in this thesis; as much as the teaching of organic chemistry in University
welcomes a novel approach to address some problems as outlined in Chapter 1 (Section
1.3 – Problem Statements). The work described in this thesis combines strengths of
these various approaches including the abstraction of simulation results for generating
explanation simple enough for chemistry students to understand, the use of qualitative
models to represent a chemist’s mental model to explicate the system behaviour of
organic reaction mechanisms, the use of causal theories for learning a topic in organic
chemistry, and the use of qualitative models to derive system behaviour and generate
explanations.
2.4 Analyzing Domain Suitability
Work on a qualitative simulator in the domain of organic reactions has not been
recorded in available literature. Hence, a preliminary study was first carried out before
the full research is embarked upon. The suitability of applying qualitative reasoning in
the problem domain is accordingly the motivation for the development of this project.
Organic chemistry is the study of carbon compounds. The study normally includes
examining the molecular structure and the chemical bonding of the compounds.
39
Organic chemistry is a science that deals with the composition, structure and properties
of substances and of the transformations that they undergo. When the organic chemists
want to create a novel compound, they would first draw the reactant structure and then
draw the structure of the compound they want to create, i.e. the product. They then
work out possible mechanisms from reactant to product. If there are a few possible
mechanisms, they would test them by doing experiments. In this scenario, the chemists
are doing organic synthesis by following the mechanisms they proposed. Most of the
time, the organic chemists could work out the mechanisms by only using commonsense
developed from chemical intuition and knowledge.
Even though the number of known organic compounds is more than 10 million, they
belong to a relatively few structural types and there are even fewer reaction types than
structural types. This makes “process generalization” possible and easier. Functional
groups are the structural units responsible for a given molecule’s chemical reactivity
under a particular set of conditions. Examples of common functional groups in organic
chemistry are alkenes, alkynes, alcohol, amines, amides, ketones, phenol and thiol.
We believe that chemical principles can be modelled and explained qualitatively by the
modelling constructs of QPT to enable the lowest level of reasoning. Traditional
approaches have not been successfully addressed the issue. It will be shown in this
thesis that the explanation follows almost isomorphically from the underlying QPT
reasoning. As a result, there is little need for complicated explanation generation
facilities.
40
2.4.1 Explaining Organic Chemical Reactions in the Classroom
When we asked the question: “How do chemistry lecturers explain organic reactions in
the classroom?” the answer is that most of the instructors use chalk and board and show
the students how to use arrows to indicate movement of electrons. They might show
some animation (which can be downloaded from the Internet) or they use PowerPoint
and painstakingly use the animation in the PowerPoint to show where electrons flow.
The lecturers at the University of Malaya and Universiti Tenaga Nasional do not use
any software that can suggest and explain a reaction from the “mechanism” point of
view. There is nothing wrong with organic chemistry instruction, but the explanation in
classroom lacks “reasoning”. The work described in this thesis is the first use of QR to
predict organic mechanisms and to explain and justify each reaction step in the entire
reaction route leading from the substrate until the most stable product is formed. The
program LHASA (Logic and Heuristics applied to Synthetic Analysis) developed by the
group under the leadership of Professor Corey at Harvard University uses AI techniques
to discover sequences of reactions which may be used to synthesize a compound.
However, the program does not show or suggest the mechanism used in a synthesis.
LHASA will be discussed separately in Section 2.7.1.
2.5 Use of Artificial Intelligence in Organic Chemistry
Application of Artificial Intelligence (AI) to solve chemistry problems started sometime
in the 1970’s. Computer applications mean that two components are needed in problem
solving, i.e. computer and data. There are two key aspects of AI research in chemistry.
These are knowledge representation and knowledge manipulation. Among the
prominent knowledge representation schemes are production rules, semantic nets,
neural nets, and state-space representation. There are, on the other hand, a few
41
knowledge manipulation techniques such as state space search and backward chaining.
In our approach, the reasoning engine is based on a chosen ontology (to model chemical
principles) and a suite of QR algorithms (to use the knowledge as if it is done by a
human chemist). To date, QR approach of reaction mechanism simulation and
explanation has not been investigated.
2.5.1 The Traditional Knowledge-based Approach
Existing knowledge-based systems for organic chemistry are not using qualitative
reasoning as the problem solving technique. Current Knowledge Based Systems (KBS)
or Expert Systems (ES) for solving organic chemistry problems are still very much
relying on precoded facts and rules, and search techniques. These programs will search
the entire knowledge base (KB) for possible “conclusions” for a given problem. As a
result, programs are not able to recognize and process inputs that are not stored in the
knowledge base. Explanation generation is also a great challenge to this type of
programming and development paradigm. There is almost no evaluation of the efficacy
of the traditional approach based on the generated explanation. Evaluation that can be
found is on the generated outputs (not on the system-based or domain-based
explanation).
2.5.2 The Machine Learning Approach
Machine learning is a broad field which includes methodologies such as neural
networks, genetic algorithms, symbolic inductive learning, explanation-based learning
and conceptual clustering. Techniques described in (Dolata, 1998) are self-organizing
map, neural networks and genetic algorithms. These techniques are sub-symbolic and
42
they rely much on the massive precoded facts and rules. The two general purposes for
which machine learning has been used in computational chemistry are the classification
and generalization of data (Rose, 1998; Zupan, 1998). The common theme shared by
the use of machine learning is that it is used to extract regularity from data. There are
many expert systems in chemistry, including toxicological systems, structure
elucidation, and reaction mechanism analysis. Nevertheless, these systems are not based
on qualitative representation and reasoning for describing and manipulating chemical
knowledge. All the above approaches cannot generate explanation dynamically to
explain the why, why-not, and how of a particular question. This is mainly because
systems that use precoded facts and rules cannot provide explanation that is both natural
and causal in nature, but our approach has made this type of explanation possible.
Figure 2.1 summarizes the benefits of applying QR approach in the organic chemistry
domain.
Figure 2.1 Some of the benefits of applying qualitative reasoning to a chemical system simulation.
Why Qualitative Reasoning?
Represents conceptual knowledge explicitly
Supports causal
explanation
Supports behaviour
prediction of chemical system
Provides good means to represent mental attributes
Allows partial
knowledge reasoning
43
2.6 Molecular Representation Schemes
Two standards for representing chemical molecules as linear structure (in software) are
reviewed. The two standards are: (1) Simplified Molecular Input Line Entry System
(SMILES) and (2) International Chemical Identifier (InChI). The two schemes were
studied for the purpose of finding one standard for the internal representation of organic
substrates used in this work.
2.6.1 The Simplified Molecular Input Line Entry System (SMILES) Codes
SMILES (Daylight Chemical Information Systems Inc.,
http://www.daylight.com/smiles/) has been surveyed. The purpose of SMILES is to
provide a simplified way for entering complex organic molecules and at the same time
reducing the storage capacity taken up by large volume of organic compounds. The
SMILES standard is not needed since this work will limit the types and families of
organic substrates, and the structures selected are not complex. Moreover, SMILES
code is more suitable for use in exchanging data format over the World Wide Web, but
the ultimate system is not meant for storing organic compounds for Web retrieval.
Another reason for not using the syntax proposed in SMILES for the input strings is that
the conversion from SMILES to IUPAC1 names will incur extra processing time. Table
2.1 shows some examples of SMILES codes of molecules.
1 IUPAC stands for International Union of Pure and Applied Chemistry. It is recognized as the world authority on chemical terminology and nomenclature. This nomenclature provides a unique name to each chemical structure.
44
Table 2.1: Some examples of SMILES codes. Atom/Molecule SMILES Name
H3O+ [OH3+] Hydronium cation CH3CH3 CC Ethane CO2 O=C=O Carbon dioxide CH3CH2OH [CH3] [CH2] [OH], or
CCO Ethanol
CH4 C Methane NH3 N Ammonia HCl Cl Hydrochloric acid H2O O Water H2S S Hydrogen sulfide H+ [H+] Proton OH- [OH-] Hydrogen anion Fe2+ [Fe++] Iron (II) cation CH2=CH2 C=C Ethane H2 [H] [H] Molecule hydrogen CH3CH2CH3 CCC Propane CH3CH3CH3COH CCCCO tert-butyl alcohol
2.6.2 International Chemical Identifier (InChI)
InChI is a textual identifier for chemical substances, designed to provide a standard and
human-readable way to encode molecular information and to facilitate the search for
such information in databases and on the web
(http://en.wikipedia.org/wiki/International_Chemical_Identifier). InChI was developed
in cooperation of IUPAC and National Institute of Standards and Technology (NIST).
It provides a way of describing chemical structures in text. The InChI string is
completely derived from the structure of a compound. One unique feature of the InChI
format is to assign the same InChI string to a compound regardless of the way it is
drawn. InChI can thus be seen as akin to a general and extremely formalized version of
IUPAC names. InChi notation can express more information than the simpler SMILES
notation and differ in that every structure has a unique InChI string which is important
in database applications. For the same reason given in Section 2.6.1, InChI is also not
used in this work. Table 2.2 compares InChI to SMILES chemical format. SMILES
code is normally not unique but has the possibility of a canonical form that is unique for
45
each structure. On the other hand, InChI formats separate the information about atoms
and bonds and thus their reading by human requires some knowledge of the format.
Table 2.2: Comparison of InChI to SMILES formats.
InChI SMILES
Linearized Yes Yes
Unique, canonical Yes Possibly
Human readable Hardly Easily
Includes atom coordinates No No
(Source:http://www.inchi.info/inchi_comparison_en.html)
2.7 Related Works
Two systems that share some similarities with our work (in terms of the application
domain and the technique used) are reviewed. The first system is LHASA
(http://derek.harvard.edu/). LHASA is an expert system using a database of retro-
reactions (called transforms). It has been under development at Harvard since late
1960’s. LHASA is one of the first computer programs for synthesis planning. The
project has produced over 20 PhD graduates. In the discussion the differences between
the system and ours will be highlighted, in terms of the techniques used in the
development. The second system is the QALSIC program. Even though QALSIC uses
the same technique (i.e. QPT reasoning) as we do, it was implemented differently in
software. The purpose of the program is to perform inorganic chemistry simulation (and
not organic reaction mechanism). This thesis will fill the gaps left by the QALSIC
program (in terms of model automation, knowledge structuring, and knowledge
validation).
46
2.7.1 LHASA
The LHASA program utilized heuristics provided by human experts, i.e. a number of
expert chemists. These heuristics gave a numerical estimate which may be used to
inform if a synthetic plan was progressing in the right direction. Originally, LHASA
only generated retrosynthetic routes. More recently it included a few software modules.
Examples of the modules are APSO for teaching of organic synthesis, PROTECT for
functional group protection, DEREK for toxicology prediction and LCOLI (LHASA for
Compound Libraries) for compound library generation. A proprietary language called
CHMTRN (CHeMistry TRaNslator) is used for the knowledge base development. The
knowledge base contains “rules” which dictate LHASA’s behaviour towards a target
molecule. The transform descriptions are an integral part of the knowledge base. When
LHASA reads a transform entry, it finds instructions (e.g. to build a precursor from the
target structure) and acts accordingly. The work described in this thesis, however, is to
predict and explain the target molecule (forward planning).
LHASA relied heavily on experienced chemists to find and select the best retrosynthetic
routes in an interactive and time-consuming manner. LHASA incorporated a more
complex strategy system which included multi-step plans based on useful reactions,
such as the Diels Alder reaction and the Robinson annulation. This allows the program
to rapidly find synthetic sequences which make useful changes. However, there are
some associated problems with this approach. According to the reference site, the long-
range transforms, which were created based on the expectations of a small set of expert
chemists, took as much as six months to prepare, and the program could easily give
cumbersome plans for molecules that contained unusual or unforeseen combinations of
functional groups. In addition, the modules were not dynamically updated when new
reactions were added so the modules slowly slipped out of date as new reactions were
47
discovered. As such, for sustainability purposes, as more synthesis and hence new
compounds are anticipated, information and search updating could be a burden (in
terms of time and effort incurred). Nevertheless, LHASA is still in use today, and new
strategic modules are still being added. The group is continuously seeking collaboration
for additions to the knowledge base and enhancement of the CHMTRN language. In
the course of development of the LHASA program, the knowledge base organization
has become very complex. This will not happen to our system since there is no
precoded solution or any reaction route kept in the knowledge base, rather only
chemical theories and basic facts required to perform the organic reactions defined in
the scope of this work. Consequently, less storage space is taken up. The system relies
heavily on expertise of chemist to select the order of the generated precursor molecules
to process further. It is believed that the QPT-based simulation is able to get rid of the
drawback outlined above. LHASA’s methods to overcome the problems include: (a)
Generating precursors for a synthetic target using all available tactics instead of a single
user-selected tactic, (b) Generating precursors automatically to find a solution sequence,
(c) Storing results in a relational database of essentially unlimited size, (d) Developing
algorithms and heuristics that emulate the decisions of an expert user in selecting which
precursors to process further.
2.7.2 QALSIC
QALSIC is among the earliest applications of QPT in chemistry for the qualitative
simulation of a small set of inorganic chemical reactions. QALSIC has managed to
break the proof-of-principle question of how inorganic chemistry can be presented in
qualitative terms especially in reasoning on its dynamic processes (such as precipitation
and dissociation). Although the QALSIC related literature claimed that the system is
48
able to simulate unknown reactants (substances whose name are not found in
knowledge bases), further examination reveals that, the system can make correct
prediction only if the chemical equation has the pattern “AB + CD � AD + CB”; i.e.
direct cross-linking of elements is obeyed. Furthermore, even with known reactants
prediction can still be erroneous. Sample reactions with erroneous answers are given in
Table 2.3. In the software, the equation balancing task is well-handled by the encoded
chemical theories and facts in QPT. For example, the system uses the orbital
information for checking the valence electrons per atom and the oxidation number is
used for assigning charges to respective ions. Nevertheless, a number of limitations
remained open, as discussed in the following three subsections.
2.7.2.1 Limitations and Problems in the QALSIC Program
Briefly, QALSIC does not cater for knowledge validation. This is the main reason why
wrong results are returned. Although the QALSIC program checks the type of a
substance, not all substances in the periodic table are included in the knowledge base.
Furthermore, only two processes were fully implemented in software (precipitation and
dissociation). Processes in QALSIC are precoded (in terms of the five-slot template).
This is because QALSIC software does not have a model construction module like the
one embedded in the QRiOM software. In addition, most of the explanations are
handcrafted. In contrast, QRiOM is able to construct qualitative models at runtime and
to provide various forms of explanation on demand.
All of the processes are precoded and that is why some experiments are restricted. The
software will not necessarily succeed in simulating a reaction. As a result, the system is
unable to predict correctly (and reasonably) for a large number of chemical equations.
49
Table 2.3 shows some of the tested chemical reactions. Our further investigation shows
that the wrong predictions are caused by the nature of the inorganic chemical reactions
(Section 2.7.2.2).
Table 2.3: Comparison of the actual and simulated results for a selected sample of inorganic chemistry reactions.
Chemical reaction Actual result Result given by QALSIC Correct/
Wrong
2Mg(s) + O2(g) 2MgO(s) MgMg, and the message: “no element to dissociate”.
�
2CO(g) + O2(g)
2CO2(g) No answer returned, and a message that says “dissociation” begins. In fact, 2CO does not dissociate.
�
Fe(s) + S(g)
FeS(s) FeS(s) ����
CaO(s) + CO2(g)
CaCO3(aq) 2CaO + C2O �
SO2 (g) + H2O(l)
H2SO3(aq) 2H2S + 4H2O �
Na2O(s) + H2O(l)
2NaOH(aq)
Na2O + H2O �
SO3(g) + H2O(l)
H2SO4(aq) H2SO3 + O “O” never exist
�
Ne + F2 No reaction Nil, and the message “F2 cannot dissociate”. The message given is actually not the real reason.
�
Ba(s)+ ZnSO4(aq) BaSO4(aq) + Zn(s) Ba + Zn4SO � 2Al(s) + 6HCl(aq) 2AlCl3(aq) + 3H2(g) AlCl3 + 3H � HCl(aq) + KOH(aq) KCl(aq) + H2O(l) KCl(aq) + H2O(l) ���� BaCl2(aq) + 2AgNO3(aq)
2AgCl(s) + Ba(NO3)2(aq) 2AgCl(s) + Ba(NO3)2(aq) ����
AgNO3(aq) + NaCl(aq)
AgCl(s) + NaNO3(aq) AgCl(s) + NaNO3(aq) ����
CaO(s) + 2HCl (aq)
CaCl2(s) + H2O(l) CaCl2(s) + H2O(l) ����
K2S + 2HCl H2S(g) + 2KCl H2S(g) + 2KCl ����
Ba(s) + 2H2O(l) Ba(OH)2(aq) + H2(g) BaO + 2H � Zn + BaSO4
no reaction (since Zn is a less active metal than Ba)
Zn SO4 + Ba �
In Table 2.3, many incorrect results were produced (by QALSIC). The program was
not able to predict correctly for a large number of chemical reactions due to the
difficulty in associating processes with inorganic elements (refer to Section 2.7.2.3 for
further explanation). We had tried devising a scheme that can correlate reaction types
50
and processes. The intention is to classify experiments (hence reactions) so as to come
out with a classification scheme that looks like the one shown in Figure 2.2.
Experiment Types
Type-I (double-displacement) Type-II (single-displacement) …. Type-N
Process-I Process-II Process-III Process-II Process-IV ……
Figure 2.2 A proposed scheme to classify inorganic experiment types.
If such a classification scheme does exist, then it would provide some help in choosing
the correct chemical processes for the inorganic reactants. However, it is very difficult
to associate each inorganic reactant to the processes (e.g. Process-I, etc. in Figure 2.2).
The reason is that chemical reaction for inorganic reactants is largely determined by the
substance involved in a reaction. In other words, every analysis must go down to the
basic elements (e.g. atoms or ions). This condition is named as “substance-dependent
syndrome”, in which, most of the time, the following statement does not hold: “if you
are in this group, you will exhibit this particular behaviour, and possess properties A
and B, then undergo processes C and D”. More results of analysis are presented in the
next two subsections in order to justify our claim that “qualitative reasoning is less
suitable for modelling inorganic chemical reactions”.
2.7.2.2 Organic Reactions Versus Inorganic Reactions
In an organic reaction, the simulation focus is on the entire organic compound (the main
chain and the structural units). When performing reasoning, it is more towards
molecule-centred reasoning. On the other hand, in inorganic reactions, reasoning is
done on the individual atoms or ions. Therefore, when it comes to modelling and
51
reasoning, the way of characterization is different. In the organic reaction, we observe
changes made to the whole compound (that serves as the initial substrate) with
particular emphasis on the nucleophile being substituted, whilst in inorganic reaction
the final products are a mixture (exchange) of several subunits of the reactants.
Qualitative reasoning is best suited to domains and subjects that meet two basic criteria.
First being the problem description is qualitative in nature, and second is the degree of
generalization should be high, meaning the model is a logical consequence to a large
number of possible values. It seems that the latter requirement is not found in
qualitative simulations for inorganic chemical reactions, where classifying the
experiment types cannot help generalize their chemical processes (and chemical
principles). There are no pre-defined processes that can be associated with each
reaction type. Even within the same reaction type, the chemical equations can only be
constructed (hence simulated) after examining individual atoms and ions that formed
these substances. Different substances exhibit different chemical behaviour even
though they are in the same class (e.g. same column or row in the periodic table). In an
interview conducted, when asked about what processes are occurred when given some
reactants, the answer from chemists is that they have to look at the specific reactants to
determine the reaction type (e.g. acid + base or single-displacement) and then study the
properties of the substances. This answer suggests that prediction of inorganic chemical
reactions requires elementary level of analysis. In many occasions, general chemical
principles cannot be used in qualitative simulation of inorganic chemical reactions. As a
result, a system that is based on QR approach for predicting inorganic reaction cannot
generate reliable results. To resolve this problem, the system can be filled with as many
specific cases (chemical facts) as possible, without which the system cannot simulate all
52
reactions without errors. The system will eventually become a database, losing its
“intelligent” nature.
2.7.2.3 Inorganic Reactions in Qualitative Reasoning: The Problems
In this section, multiple cases of inorganic reaction with their associated problems (also
typical problems in QALSIC) are demonstrated. We then moved on to relate these
problems with the claim made in Section 2.7.2.2. Inorganic chemistry reactions can be
classified by the nature of the reaction. There exist five chemical reaction types (Murov
and Stedjee, 1997; Peller, 1997; Peller, 2003; Murov and Stedjee, 1997), namely the
Synthesis (or combination) reaction, Decomposition reaction, Combustion reaction,
Single-displacement reaction and the Double-displacement reaction.
A synthesis reaction takes two reactants to give one product. Typically the chemical
equation is written as “A + B � AB”. Synthesis reaction can further be divided into
types (i) to (v) below. Note that s=solid, l=liquid, aq=aqua, and g=gas. Representatives
of synthesis reactions are given as follows:
(i) Metal + oxygen →∆ metal oxide 2Mg(s) + O2(g) →∆ 2MgO(s) 4Al(s) + 3O2(g) →∆ 2Al2O3(s) 4Li(s) + O2 (g) →∆ 2Li2O(s)
(ii) Nonmetal + oxygen →∆ nonmetal oxide N2(g) + O2(g) →∆ 2NO(g) S(s) + O2(g) →∆ SO2(g) 2CO(g) + O2(g) →∆ 2CO2(g)
53
(iii) Metal + nonmetal ���� salt
2Na(s) + Cl2(g) � 2NaCl(s) 2Al(s) + 3Br2(l) � 2AlBr3(s)
Fe(s) + S(g) � FeS(s) Mg(s) + Cl2 (g) � MgCl2(s) The reactions outlined above do not require qualitative reasoning since the reaction is
straightforward, i.e. when a metal reacts with oxygen, metal oxide is the product and
when a non-metal reacts with oxygen, one will get non-metal oxide as product. The
process needed is just one, namely the “oxidation”. In type (iii), the process is rather
straightforward, i.e. formation of precipitation. This type of reaction does not require
qualitative reasoning since it needs neither chemistry expertise nor commonsense to
solve it. All of these are dependent on the specific substance used. In experiment
subtypes (iv) and (v) below, “hydrolysis” is the process that each reaction will undergo.
(iv) Metal oxide + water ���� metal hydroxide
Na2O(s) + H2O(l) � 2NaOH(aq) CaO(s) + H2O(l) � Ca(OH)2(aq) MgO(s) + H2O(l) � Mg(OH)2(s) (v) Nonmetal oxide + water ���� oxy-acid
SO3(g) + H2O(l) � H2SO4(aq) N2O5(s) + H2O(l) � 2HNO3(aq)
However, when looking at the equations in (iv) and (v) above, there is no specific rule
to follow. What are the specific processes and in what sequence do these processes
occur? Can they be applied to all reactions that fall under the synthesis reaction? The
answer is “no”. When this cannot be done then the only way to model its behaviour is
by the traditional rule-based approach, i.e. store as many examples as we can in the
knowledge base. This is surely not practical because there are many metal (non-metal)
oxides. Qualitative reasoning is again not suitable to predict the outcomes. Each of the
above reaction employs different processes to yield the final products. That is, merely
knowing the experiment type cannot help to determine the processes to be applied
54
because the nature of inorganic substance reaction is substance dependent. As a result,
no generalization can be done. This is true for all of the main reaction types and not
only the chemical reactions classified as synthesis.
Next, some examples of the Single-displacement reaction type (or substitution) are
analyzed. This reaction involves an element (e.g. A) with a compound (e.g. BC) such
that the element (A) replaces one of the elements (B or C) in the compound. There exist
two formulas: “A + BC � B + AC” (if A is a metal and more reactive than B) and “A +
BC � C + BA” (if A is a halogen, it will replace C to form BA, provided A is a more
reactive halogen than C).
Single-displacement reaction:
Ba(s)+ ZnSO4(aq) � BaSO4(aq) + Zn(s) Zn(s) + 2HCl(aq) � H2(g) + ZnCl2(aq) H2(g ) + CuCl2(aq) � Cu(s) + 2HCl(aq) 2Al(s) + 6HCl(aq) � 2AlCl3(aq) + 3H2(g) Cl2(g) + CuBr2(aq) � CuCl2(aq) + Br2(aq)
Modelling this class of reaction is somewhat easier because the rules can be applied to
most cases, but not all. There are still reaction cases that violate the rules. For
example, the following pairs of substances do not react.
No reaction cases: Zn + BaSO4 � no reaction (since Zn is less active metal than Ba) Cu(s) + ZnCl2(aq) � no reaction (since Cu is less active metal than Zn)
We envisage that a qualitative reasoning engine will generate incorrect result for the
above reactions (“Zn + BaSO4” and “Cu(s) + ZnCl2”), since none of the two general
formulae (“A + BC � B + AC” and “A + BC � C + BA”) can be used. This is why
QALSIC produces wrong result for “Zn + BaSO4” reaction (Table 2.3); simply knowing
that Zn and Ba are metals is not enough to model its behaviour correctly. Let us show
55
another single-displacement reaction example. Note that the answer is not “B + AC”,
“C + BA”, or “no reaction”. The product of this reaction is “Ba(OH)2 + H2” instead.
Ba(s) + 2H2O(l) � Ba(OH)2(aq) + H2(g)
A + BC
This is because very active metals (e.g. Barium) can even displace hydrogen from
water, making it not obeying any general chemical principles. For cases like this, it is
suggested to treat them as special cases in the software and not by qualitative reasoning
approach.
We now examine some examples in the Double-displacement reaction (or Metathesis).
Double-displacement reactions can further be divided into types (i) to (iv) below.
Reactions in this class involved two ionic compounds in an aqueous solution and
usually one of the products is a compound insoluble in water (precipitate), a gas or a
slightly ionized compound (Goldberg, 1998). Typically the chemical equation is
written as “AB + CD � AD + CB”, i.e. it involves an exchange of positive and
negative groups. Examples in (i) show acid-base neutralization processes.
Neutralization means there is H+ from the acidic solution reacting with OH− from the
basic solution to form the primary product H2O and side product salt.
(i) Acid-base Neutralization HCl(aq) + KOH(aq) � KCl(aq) + H2O(l) H2SO4(aq) + Ca(OH)2(aq) � CaSO4(aq) + 2H2O(l) HCl(aq) + NaOH(aq) � NaCl(aq) + H2O(l)
However, some reactants are not seen as common acid or basic but they do give rise to
water and salt combination. Examples are:
Na2O + 2HCl � 2NaCl + H2O(l) 2NaOH + CO2 � Na2CO3 + H2O(l) [H+ is not seen in the equation, but when CO2 is dissolved in water, H+ will exist]
56
Same situation interpretation goes to examples in subtypes (ii) and (iii), as given below.
(ii) Metal oxide + Acid CuO(s) + 2HNO3(aq) � Cu(NO3)2(aq) + H2O(l) CaO(s) + 2HCl (aq)� CaCl2(s) + H2O(l) [When oxides of metals are added in water, bases are formed] (iii) Nonmetal oxide + Base SO3 + 2KOH � K2SO4 + H2O CO2 + 2Ca(OH)2 � CaCO3 + H2O [When oxides of non-metals are added in water, acids are formed]
If the above reactions are modelled based on their general chemical principle, i.e. <acid
+ base> pair will give <water + salt> as the product pair, then the system is (again)
unable to return correct answers unless all reactants that will become acid and basic in
water are stored as chemical facts.
Modelling double-displacement reaction examples shown in (iv) requires very minimal
effort since the “AB + CD � AD + CB” formula can be applied rather throughout. In
most cases, the products can be determined from the knowledge of the ionic charges of
the compounds.
(iv) Precipitation Reactions
E.g. Formation of an insoluble precipitation BaCl2(aq) + 2AgNO3(aq) � 2AgCl(s) + Ba(NO3)2(aq)
AgNO3(aq) + NaCl(aq) � AgCl(s) + NaNO3(aq)
BaCl2(aq) + Na2SO4(aq) � BaSO4(s) + 2NaCl(aq)
Ca(NO3)2(aq) + Na2C2O4(aq) � CaC2O4(s) + 2NaNO3(aq)
E.g. Formation of gas H2SO4(l) + NaCl(s) � NaHSO4(s) + HCl(g)
K2S + 2HCl � H2S(g) + 2KCl
57
However, there are still cases that do not follow the general principles. This is
illustrated by the following double-displacement reaction examples. In principle, the
following reactions should give two and only two products but they give three products.
NH4Cl + NaOH � NaCl + NH3(g)+ H2O (3 products) H2SO4 + Na2CO3 � Na2SO4 + H2O + CO2(g) (3 products) K2CO3(aq) + 2HNO3(aq) � 2KNO3(aq) + H2O(l) + CO2(g) (3 products)
Let us take another example: “CaO + CO2” is supposed to give “CaO2 + CO” as
products, since “CaO + CO2” has the format of “AB + CD” but the reaction formula
“CaO + CO2 � CaO2 + CO” is invalid. A reasoning engine that relies solely on general
principles will not be able to predict this outcome. The correct output is CaO3.
CaO + CO2 � CaO3 (1 product)
This is because even though the reaction formula has the format of what is seen in the
left-hand side of a double-displacement reaction, it is actually a combination reaction
because both compounds contain oxygen. As one can see, the specific type of reaction
is determined by the substance in use. Furthermore, when two non-metallic elements
combine, the product formed often depends on the relative quantities of the reactants
(refer to the reactions shown below), i.e. quantitative data is needed.
C(s) + O2 (g, excess oxygen) →∆ CO2(g) 2C(s) + O2 (g, limited quantity of oxygen) →∆ 2CO(g)
The “C(s) + O2” reaction is to subject carbon in flowing oxygen. Here, the number of
moles is not counted but practically, in the volume of O2 used. In the “2C(s) + O2”
reaction, the number of moles is usually computed and changed to volume then only
that much of O2 is introduced in the reaction. The second equation reads “2 moles of
carbon reacts with 1 mole of oxygen to produce 2 moles of carbon monoxide”. So, if 2
58
moles of CO (=12.01+16.0 = 28.01g/mol) are required, 1 mole of oxygen (32g) is
needed and carbon is used in excess, such as a little bit more than 2 moles (= 24.2g).
Prediction cannot be done by using commonsense knowledge about chemistry. In this
case, quantitative data is required to simulate and return the correct answer. There is
another case where general principle cannot be applied. The reactants Ne and F2 in the
equation “Ne + F2(g) � nr (no reaction)” will not result in any reaction, simply because
the gas neon is too stable. In such case, what is required is a simple look up table and
no reasoning is needed.
2.7.2.4 Discussion
There are many inorganic reactions that do not follow general chemical rules. For such
reactions, QPT is not the right reasoning technique to be used because the formation of
final products is not merely from one’s chemical intuition. It is difficult to associate
each reactant to the experiment types, otherwise the following statement will hold: “if
object A belongs to the same class of another object, then object A will behave similarly
as the other object in that class”. But this is not true for inorganic reactions. For
instance, when an individual is identified as “ion”, one still needs to look at what is the
specific ion (e.g. Cl−, Mg2+). So, it can be concluded that the reason why QALSIC
made some wrong predictions is largely due to the nature of the problem domain, rather
than the limitation in the QPT representation. This is the main reason that QALSIC is
unable to predict correctly for substances unknown to the system. Simply said, there are
many exceptions to the general rules of inorganic chemistry. We will show in Chapter
3 and Chapter 4 that organic chemical reactions are much easier to be classified into
generic structural types (groups) compared to inorganic reactions. As long as a
compound contains a particular functional group, it will possess the same chemical
59
property hence similar behaviour and as such the general principles can be applied
throughout all organic compounds that contain the functional group. Furthermore, two
reusable chemical processes have been identified for simulation use, namely the “make-
bond” and the “break-bond” processes, which will be discussed separately in
subsequent chapters. In summary, qualitative reasoning is not suitable for inorganic
reaction simulation because generalization is difficult to accomplish. If one proceeds,
there are two options, firstly to limit the number of experiments and secondly to adopt
the traditional rule-based approach.
2.8 Conclusion
This chapter reviewed relevant literature on qualitative reasoning applications in science
and engineering. The chapter also reviewed AI applications in chemistry. A
comprehensive study of a related work, QALSIC has also been presented. The
QALSIC program was tested with an intention to seek the main cause in its prediction
deficiency. Our investigation showed that qualitative reasoning is less suitable for
predicting the outcome of inorganic reactions because it is more difficult to seek
chemical property generalization in this problem domain. From our discussion, it can
be said that none of the educational software for organic chemistry addresses the
simulation and explanation issues from the standpoint of qualitative reasoning. Tight
coupling between concepts and their embodiment in software is crucial in building
smart educational software that can “reason” the processes intuitively. This is important
since conceptual understanding of a subject and the ability to provide explanation are
basic requirements for effective learning.
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Chapter 3 Qualitative Modelling of Organic Reactions
3.1 Introduction
This chapter describes how the following objectives are achieved:
• To acquire human expertise in the field of organic reaction mechanisms.
• To examine QPT and use it to represent chemical theories qualitatively in order to
model the behaviour of organic reactions.
• To classify chemical processes for a variety of organic substrates in order to
promote model reusability.
• To develop an algorithm that automates model construction.
The organization of this chapter is as follows: A review of the state of the art in
qualitative modelling is presented in Section 3.2. Section 3.3 gives the procedures used
in domain knowledge acquisition. Section 3.4 – Section 3.6 present the domain theory,
the chosen reaction examples, and the underlying thought processes for organic
reactions. The approach for classifying reaction steps (organic processes) as “make-
bond” and “break-bond” is discussed with proof in Section 3.7. The identification of
individual views and their associated organic processes are also discussed since the
reproduction of the behaviour of reaction mechanisms relied on both the use of
individual views and the organic processes that occurred. Section 3.8 discusses the
design decision underlying the model automation task especially on the usage of the
modelling constructs of QPT to represent general organic reaction knowledge and
chemistry theories. Section 3.9 gives useful guidelines for modelling views for organic
61
reaction use. A handcrafted model is also presented in Section 3.10 to show how the
model is used to support learning. Section 3.11 concludes the chapter.
3.2 State of the Art in Qualitative Modelling
Much research in the field of qualitative reasoning has been committed to the questions
of representation of qualitative models. Typically, the qualitative models are used to
create the groundwork on which the quantitative models can be explained (Frederiksen
and White, 2002; Sime, 1996; Sime and Leitch, 1992; White and Frederiksen, 1990).
There are two important questions concerning qualitative modelling. First is how to
construct qualitative models, and second is how to automate model construction for the
application of qualitative modelling techniques especially for the QR technique to be of
widespread use (Bratko and Dorian, 2003a). Progress in qualitative reasoning about
physical systems has also led to new modelling languages that describe entities and
processes in conceptual terms and represent notions of causality explicitly
(Falkenhainer and Forbus, 1991; Forbus, 1996b; Weld and de Kleer, 1990).
Traditional mathematical and computer modelling languages do not attempt to
formalize such notions because they are designed for experts who already know such
things.
Bredeweg and Forbus (2003) expressed their hope to see qualitative modelling
vocabularies to be of widespread used among the educators as a new means to express
aspects of their expertise that are currently described as “intuition”. Their review also
emphasizes the importance of conceptual knowledge and causal theories in education,
particularly concerning reasoning about system behaviour. Mastering the causal
theories of physical phenomena can help students in answering fundamental questions
62
in science education (e.g. what happens? why does it happen? what does it affect?).
Our work goes along with the same emphasis. In this work, qualitative model
development involves the mapping of human reasoning model to ontological primitives
of the QPT while the role of qualitative modelling in this work is to prepare organic
processes (in terms of the modelling constructs of QPT). When reasoning is performed
on these models, the behaviour of organic reactions can be reproduced. Reproducing
the chemical behaviour of organic reactions can help predict the outcomes of a chemical
reaction.
The formalization of automated modelling techniques has been one of the hallmarks of
QR where a model for a scenario is automatically constructed from a structural
description and task constraints (Falkenhainer and Forbus, 1991). The approach used in
assembling a model (given a scenario or initial situation) in CyclePad and Garp3
(Bredeweg et al., 2007) is compositional modelling (Falkenhainer and Forbus, 1991).
More recent work done by Horiguchi and Hirashima (2009) also uses the compositional
modelling technique to provide intelligent support for authoring graph of microworlds.
Most qualitative reasoning systems adopt a reductionist view of the world and are
aimed at building libraries of independent, elementary model fragments. In
compositional modelling, model fragments are chained. This idea provides the basis for
reusing models, a highly desirable feature for industrial applications (Bredeweg and
Struss, 2003). QRiOM also uses a kind of model composition technique to construct
QPT models at runtime. The logical steps for automating a QPT model based on a
simple substrate input will be presented in the following sections.
Salles and Bredeweg (1997) said the type of user and the role of the model are
important factors for understanding the purpose of a model in a qualitative simulation
63
context. Starting with the former, it makes a difference whether the constructed model
is to be used by experienced domain experts or by students in colleges/universities. The
latter, on the other hand, suggests that a model may be used as a tool for inspecting the
dynamics of a physical system. In this situation the emphasis will be on correct and
complete simulation. In other situations however, different aspects may become more
important. Particularly, in an educational setting, the understanding of model
articulation is more important to the learner. Since our work has the role of assisting
the students, the “type of user” is an important factor in our design consideration, in that
the constructed models only consist of sufficient knowledge in understanding a
chemistry phenomenon.
More recent attempts on qualitative modelling tools are QCM system (Dehghani and
Forbus, 2009). QCM is a tool (aimed at cognitive scientisits) that allows users to create
situation-specific descriptions of physical processes rather than asking the user to first
create and then instantiate a first-principles domain theory. The use of experiential
knowledge is believed to have profound effects and consequences for human reasoning
(Forbus and Gentner, 2009). The term “dark knowledge” is used in their work to refer
to specific cases derived from personal experience or through culture. However, “dark
knowledge” is the type of knowledge that currently has no well-designed formalisms in
QR. Forbus and Gentner added that the episodic memories and experiences people
have are powerful mechanisms to construct generalizations and hence they play a
central role in human mental model.
A qualitative model can be used to generate predictions given an initial situation. This
process, as well as its result, is called qualitative simulation (discussed in Chapter 4).
The relationship between modelling and reasoning is depicted in Figure 3.1.
64
Figure 3.1 Simulation entails reasoning from model.
3.3 Domain Knowledge Acquisition
Knowledge acquisition involves the acquisition of knowledge from human experts,
books, documents, or computer files (Turban, 1999). The term knowledge elicitation, on
the other hand, often implies that the transfer is accomplished by a series of interviews
between a domain expert and a knowledge engineer who then develops a computer
program representing the knowledge. As such, domain experts (chemists) and
chemistry students were interviewed to elicit the domain knowledge and chemical
intuition they apply when solving organic reaction problems. From the interviews, it is
particularly ascertained that understanding reaction mechanisms requires the application
of chemical insight and chemical commonsense at intuitive level – a suitable application
domain for QR technology.
According to Chi et al. (1981), experts and novices categorise problems differently,
where experts tend to categorise problems using the underlying principles rather than
entities contained in the problem statement. The difference lies in the identification of
important features of the domain and the interpretation of this information. This
categorization can be seen as a means of accessing the most appropriate model of the
domain. By only selecting relevant information during the modelling process, the
resultant model is simpler and more importantly, reasoning from that model is
simplified. The work found that expert schemas contained a great deal of procedural
knowledge with explicit conditions of use. Novice schemas contained declarative
Qualitative Simulation
QPT Model
Building
QPT Model
Reasoning
65
knowledge but lacked the abstracted solution methods. Based on the findings of Chi et
al., the acquisition of expert knowledge was carried out as follows:
• Problem characteristics and the behaviour of organic reaction mechanisms were
sought and studied. This includes the conceptual understanding about organic
chemistry, organic reaction, and their mechanisms.
• The course outline of the chemistry subject was reviewed and the mental model of
a few domain experts (chemists at University of Malaya and Universiti Tenaga
Nasional) was documented.
• Dialogues with chemists were conducted to find the possibility of representing the
required knowledge in qualitative terms using QPT.
o Collecting the intuitive and causal aspects of chemists’ mental models helps
in the design of the cognitive steps used in the simulation algorithms.
3.4 Understanding Organic Chemistry Reactions
In this work, “A + B → C + D” is named as a chemical equation; “A + B” is an organic
reaction (where “A” is an organic substrate). Before a simulation can begin, the
reaction steps of a chemical equation must first be identified. For example, the
simulation of equation 3.1 can be described as a series of processes that occur and these
processes will be used to explain how the product is formed (= “the mechanism used”),
where “X” is any halogen (group VII in the periodic table). Examples of halogen are I,
Br, Cl, and F. The symbol “R” can take the forms of anyone of the following strings:
CH3, CH3CH2, CH3CH3CH, or (CH3)3C.
R−OH + HX � R−X + H2O (3.1) Alcohol Hydrogen halide Alkyl halide Water
66
The organic processes that occurred are identified as follows:
Process I: Protonation (=“make-bond”). There is a proton (H+, in our case) which is an
electrophile (electron poor species) and there is a lone-pair electron on the “O”. From
the chemical knowledge base, “OH” is a poor leaving group. The process that occurs is
“H+” sticks to “O:” (oxygen with a lone-pair) and this gives rise to “−OH2+” which is
unstable, so that the next process can begin (i.e. the “dissociation” process will occur, as
below).
Process II: Dissociation (=“break-bond”). This process describes the deletion of the
bond in the “R−OH2+” compound. This process happens because the “O” in the
compound is unstable since there are three covalent bonds (for oxygen, maximum is
two) and there should not be a positive charge on it. At the end of this step, it will
produce H2O and R+ (a stable tertiary carbocation).
Process III: Capturing of carbocation by halide anion (=“make-bond”). The process is
called upon since the reacting species are still in their unstable states with charges
around (very reactive). This step describes the formation of a covalent bond between
the two ions X− (anion) and R+ (cation). This process returns R−X (alkyl halide) as the
product.
By applying one’s chemical knowledge, after the third process, the reaction will stop
since the final products (water molecule and alkyl halide) are very stable and having
lower energy than the initial substrate. The sequence of use of the organic processes
(e.g. “make-bond”, “break-bond” and another “make-bond”) will be stored and then
67
checked with the chemical KB in order to determine the mechanism used in the
prediction of the outcomes.
3.5 Organic Reaction as Modelling Task
This section explains the fundamentals of the domain theory, namely the organic
reactions and mechanisms and how to represent them in QPT notations. QPT is
adequate for representing qualitative knowledge (Salles et al., 1996). This is because
the modelling constructs of QPT provide good means for describing processes in
conceptual terms, and embodies notions of causality which are important to explain
behaviour of chemical systems. Thus, it is useful as a language to write dynamical
theories in expressing the intuitive ideas of organic reactions. The QPT’s qualitative
proportionalities and influences are powerful primitives to be used in organic reaction
modelling in building chains of causality to describe and explain a mechanism. The
causal relationship provides good means to explain the overall change in a reaction
simulation, called “mechanism” in organic chemistry.
Three specific chemical equations (equation 3.2 – equation 3.4) will be used to show
how the general behaviour of organic reactions can be modelled. An organic reaction
usually takes place between a nucleophile and an electrophile. The three equations will
share some organic processes (hence reusability is achieved), and may use different
reaction mechanisms to yield the respective products. With a chemical reaction, we
know what to start off and after qualitative simulation we get what it finishes with (the
final product), but to understand the reaction we want to know the story in between and
this is called “mechanism”. An organic mechanism is normally used to explain how a
product is formed. The substrates (which are organic compounds) tested in this work
68
are two: (1) ROH (alcohol or alkanol), which has a functional group which contains
oxygen and a single bond. The hydroxide ion “OH−” is a strong base, hence a poor
leaving group, (2) RX (where X = F, Cl, Br or I), which is alkyl halide, involving a
functional group which contains a halogen atom. The selection of the chemical
equations is also based on the two different organic mechanisms defined in the scope of
this research. As shall be explained, process reusability can also be demonstrated by
choosing these three chemical equations. The products of equation 3.2 and equation 3.3
necessitate the SN1 mechanism while SN2 mechanism is used to explain equation 3.4.
SN stands for substitution nucleophilic and the “1” shows that the reaction is first order
or unimolecular, that is only one of the reactants affects the reaction rate. SN2 reaction
is second order since the rate is dependent on both the alkyl halide and the incoming
nucleophile. The “2” signifies that the rate of reaction is second order or bimolecular
and depends on both the concentration of the nucleophile and the concentration of the
alkyl halide. The specific behaviour of SN1 and SN2 will be discussed in Chapter 4. In
Chapter 4, we presented simulation scenario for reproducing the behaviour of SN1 and
SN2 by using the qualitative reasoning algorithm.
Most chemists will construct thought processes (a series of small reaction steps) in their
minds when solving a chemical equation and the organic reaction problem is also
solved along this line. The following chemical equations will be described as series of
small reaction steps. The thought processes for equation 3.2 – equation 3.4 are
presented in Section 3.6 (Figure 3.2 – Figure 3.4).
(CH3)3COH + HCl → (CH3)3CCl + H2O (3.2) (CH3)3CBr + H2O → (CH3)3COH + HBr (3.3)
HO− + CH3CH2Br → CH3CH2OH + Br
− (3.4)
69
3.5.1 Chemical Equation as a Reasoning Task
One of the objectives of this work is to use a QR technique to design a learning tool that
can improve students’ conceptual understanding of the subject. As such, if a student
learns what is behind the above three equations as “it is only an exchange of
nucleophile, hence it is just a futile problem”, or simply memorize “A + B” will give
rise to “C + D” then the learner would not be able to answer basic questions such as:
1. What will be the first reaction step?
2. Why did the process occur?
3. What is favourable in the step?
4. Why a bond is made at the particular atom and not at the other atom?
5. What is the main cause for the reduction of lone pair electrons on a particular atom?
6. What breaks the bond between atom1–atom2?
The QR approach provides a systematic way for converting and solving the chemical
equations so that students would know why a particular process is taking place and how
a particular outcome is produced. Deep knowledge is perceived as essential in
qualitative simulation of organic reactions. One form of reasoning with deep knowledge
is by qualitative reasoning. The qualitative reasoning approach is able to generate
explanation based on the why, what and how types of query, of which the thesis shall be
focusing and discussing in detail. We believe this approach of modelling organic
reactions will help improve a learner’s reasoning ability. An increasing level of
conceptual understanding about the subject can also be expected.
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3.6 The Underlying Thought Processes for Organic Reactions
It is important that chemists can predict whether a reaction will occur and also where it
will occur. Most reactions involve electron-rich molecules forming bonds to electron
deficient molecules (i.e. nucleophiles links to electrophiles). The bond will be formed
specifically between the nucleophilic centre of the nucleophile and the electrophilic
centre of the electrophile. For completeness, a brief definition for the two terms is
given: Nucleophiles are electron-rich molecules and react with electrophiles.
Electrophiles are electron-deficient molecules and can react with nucleophiles. The
nucleophilic centre of a nucleophile is the specific atom or region of the molecule which
is electron-rich. The electrophilic centre of an electrophile is the specific atom or
region of the molecule which is electron deficient. An electrophile will accept electrons
in order to fill up their valence shell. If a molecule has negative charge, it must be
electron-rich. It is therefore a nucleophile. The nucleophile centre will be the atom
which has the negative charge. Likewise, if the charge on a molecule is positive, it is
electron-deficient. The electrophilic centre will be the atom bearing the positive charge.
The symbol “δ+” (delta-plus) refers to a partial positive charge species (or neutral
electrophile) while “δ-” (delta-minus) symbolizes partial negative charge species
(neutral nucleophile) that has a tendency to pull electrons towards it. For example,
O−H bonds are polar covalent because the oxygen atom is significantly more
electronegative than the hydrogen atom. As a result, the oxygen atom has a greater
share of the electrons in the O−H bond and is slightly negative (i.e. δ-). To provide an
example of δ+, let us use C−X (X is a halogen). The carbon (C) is δ+ since it has a
lesser share of electrons (less electronegative) in the C−X bond.
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Thought processes for equation 3.2: The chemical equation “(CH3)3COH + HCl →
(CH3)3CCl + H2O” describes a functional group transformation reaction, where
nucleophilic substitution (halogen substitution) is the mechanism for obtaining the final
product. Halogens are atoms in the 7th column of the periodic table. The series of the
small reaction steps involved in converting the starting material ((CH3)3COH, a tertiary
alcohol) to the final product ((CH3)3CCl, alkyl halide) is depicted in Figure 3.2. Note
that double dots represent the electrons associated with the particular atom in the
molecule.
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O =nucleophilic centre H+=electrophile
.. .. ..+ ..
(CH3)3C – O: + H – Cl: ↔ (CH3)3C–O–H + :Cl:-
|
.. | ..
H H tert-butyl alcohol hydrogen chloride tert-butyloxonium ion chloride ion
(a) Reaction step 1
C = δ+ O = δ-
..+ .. (CH3)3C– O–H ↔ (CH3)3C
+ + :O–H
| |
H H tert-butyloxonium tert-butyl cation water
(b) Reaction step 2 C
+ = electrophilic centre Cl
−= nucleophile
.. ..
(CH3)3C+ + :Cl:
-
→→→→ (CH3)3C–Cl: .. .. tert-butyl cation chloride ion tert-butyl chloride
(c) Reaction step 3 Name of the chemical process Reactant 1 Reactant 2
Protonation
(CH3)3COH (nucleophile)
H+
(electrophile) Dissociation
(CH3)3C–OH2+
Capturing of anion by carbocation
(CH3)3C+
(electrophile) Cl
−−−−
(nucleophile)
(d) Reactants and their associated chemical processes
Figure 3.2 The conversion of a tertiary alcohol to yield alkyl chloride can be described as a series of three small steps.
The reaction steps are explained below.
• Step 1: Protonation of tert-butyl alcohol to produce an oxonium ion. The curved
arrow sign that starts from a double-dot (“..”) and points to an atom means donating
electrons to form a covalent bond while the (curved) arrow pushing sign that starts
73
from a link (“−”) and points to an atom means breaking the link to donate electrons
to the atom.
• Step 2: Dissociation of tert-butyloxonium ion to produce a carbocation. Note that
in “(CH3)3C–OH2+” the “O” pulls electrons to it because by doing so it will produce
a stable and neutral product (water molecule).
• Step 3: Capture of tert-butyl cation by chloride ion.
Thought processes for equation 3.3: The chemical equation “(CH3)3CBr + H2O →
(CH3)3COH + HBr” describes the production of alcohol from an alkyl halide. The final
product is obtained through a series of reaction steps as depicted in Figure 3.3. The
reaction steps are explained below.
• Step 1: Dissociation. “Br−” is a weak base thus a good leaving group.
• Step 2: Reaction with water.
• Step 3: Fast acid-base reaction.
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C = δ+ Br =δ-
(CH3)3C Br ↔ (CH3)3C
+ + Br−
(a) Reaction step 1
C = electrophilic centre O = nucleophilic centre .. (CH3)3C
+ + : O – H → (CH3)3C – O+– H | | H H
(b) Reaction step 2
H = electrophilic centre O = nucleophilic centre
.. .. .. .. (CH3)3C – O+ – H + : O – H ↔ (CH3)3C – O– H + H – O+ – H | | . . | H H H
(c) Reaction step 3
Name of the chemical process Reactant 1 Reactant 2
Dissociation
(CH3)3C–Br
Reaction with water
(CH3)3C+
(electrophile) H2O
(nucleophile) Fast acid-base reaction
(CH3)3COH2+
(electrophile) H2O
(nucleophile)
(d) Reactants and their associated chemical processes
Figure 3.3 The production of a tertiary alcohol can be described as a series of three reaction steps.
Thought processes for equation 3.4: The chemical equation “HO−
+ CH3CH2Br →
CH3CH2OH + Br−” describes the substitution of a nucleophile (Br, a leaving group) by
an incoming nucleophile (OH−), as shown in Figure 3.4. In chemical theory, the two
steps are concerted, i.e. the bond formation and bond cleavage happen at the same time.
But in the QPT representation (and thus program development), these two processes are
75
assumed to occur at two different time instances. First, break a bond (bromine acts as a
δ- and the carbon bearing the least number of hygrogen substituents acts as a δ+), then
make a new bond (the nucleophile is the OH− while the carbon attached to the bromine
atom is the electrophilic center). Overall, the presentation of such thought processes
allow easier identification of individual views needed in each process that occurred.
δ- δ+ δ-
HO− + CH3CH2Br → [HO
- ----- CH2CH3 -----Br]
Transition
→ CH3CH2OH + Br−
Final products
(a) Concerted steps
Name of the chemical process Reactant 1 Reactant 2
Dissociation
Br–CH2CH3 Br (δ-) C (δ+)
Reaction with HO−
C+ H2 CH3
(electrophile) HO− (nucleophile)
(b) Reactants and their associated chemical processes
Figure 3.4 The “dissociation” and “reaction with HO−” are concerted steps. This is a typical SN2 backside attack reaction.
3.6.1 Individual Views Identification
The view identification technique has just been presented in Section 3.6. Analysis
showed that only two chemical processes are required for the entire simulation to
reproduce the behaviour of the specified organic mechanisms. First, we discuss the
individual views, then the two chemical processes. In QPT, an individual view is to
76
describe both the contingent existence of objects and object properties that change
significantly with time. Individual views are used to model the behaviour of individuals
(objects) and to provide explanation about their general characteristics. Automatic
construction of individual views is made possible through recognizing the reacting
species as either a nucleophile or an electrophile. Our approach suggests that an organic
reaction is triggered based on the recognition of the reacting species, which is called
“view pair” in this work. Each view corresponds to a QPT’s “individual view”.
Individual views identified for equation 3.2 are the following:
• Individual-View Proton (e.g. H+)
(An electrophile used by step 1)
• Individual-View Hydroxyl (e.g. −OH)
(A nucleophile used by step 1)
• Individual-View Oxonium ion (e.g. the H2O+ in (CH3)3COH2
+ compound)
(“C” is delta-plus, “O” is delta-minus, both are used in step 2)
• Individual-View Halide-Ion (e.g. Cl−)
(A nucleophile used by step 3)
• Individual-View Carbocation (e.g. (CH3)3C+)
(An electrophile used by step 3)
Nucleophiles and electrophiles can further be classified as charged or neutral (the
states). As demonstrated in the above example, in some reactions, the reacting species
involved is a neutral electrophile (e.g. “C”) rather than a charged electrophile (e.g. “C+”
or “H+”). Also, a nucleophile can be charged (e.g. “Cl−”) or neutral (e.g. the “O” in an
alcohol oxygen) as given above. The design of the views used in this work caters for
both the charged and neutral nucleophile/electrophile. Examples of charged
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nucleophile, charged electrophile, neutral nucleophile, and neutral electrophile are given
below (in predicate logic format).
nucleophile('O', neutralNu).
nucleophile('Cl-', chargedNu).
nucleophile('Br-', chargedNu).
nucleophile('HO-', chargedNu).
nucleophile('O+', chargedNu).
nucleophile('Br', neutralNu).
nucleophile('Cl', neutralNu).
nucleophile('I', neutralNu).
nucleophile('F', neutralNu).
electrophile('H+', chargedElec).
electrophile('H', neutralElec).
electrophile('C', neutralElec).
electrophile('C+', chargedElec).
3.6.2 Representing Individual Views
Note that equation 3.2 – equation 3.4 each uses different substrates and reagents but so
long as we are able to identify what class/group each individual view belongs to then
modelling QPT processes may be automated. A representative result has been tabulated
in Tang and Mustapha (2006). The main result is that, in a particular covalent bonding,
all nucleophiles will undergo the same chemical change. Likewise all electrophiles will
follow another pattern of change. This section shows the properties of views in QPT
terms. Properties unique to electrophile (e.g. C, H+, −C+) are given in Figure 3.5 while
properties unique to nucleophile and leaving group views are presented in Figure 3.6.
These specifications can serve as generic views for any functional group defined in the
scope of this work.
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Individual-View “Nucleophile” (e.g. OH-, Cl-) Individuals
p ;a piece-of-stuff Preconditions
nucleophile (p)
Relations
Ds[charge(p)]= 1
lone-pair-electron (p) +
−P no-of-bond(p)
charge(p) −
+P lone-pair-electron (p) (a)
Individual-View “Charged-Nucleophile”
Individuals
p ;a piece-of-stuff e.g. Cl- or OH- Quantity-Conditions
charges(p, negative) Am[lone-pair-electron (p)] >= ONE
(b)
Individual-View “Delta-Minus”
(e.g. the “O” that bonds to “C” in the main chain) Individuals
p ;is a piece of stuff, the leaving group Preconditions
electronegativity(p) > electronegativity(carbon) Quantity-Conditions
leaving-group(p, good) Relations
Ds[charge(p)]= -1
lone-pair-electron(p) −
+P no-of-bond(p)
charge(p) +
−P lone-pair-electron(p)
(c)
Figure 3.5 (a) Generic definition for an electrophile described using QPT (b) An electrophile used in “make-bond” process (c) An electrophile used in in “break-bond” process.
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Individual-View “Nucleophile” (e.g. OH-, Cl-) Individuals
p ;a piece-of-stuff Preconditions
nucleophile (p)
Relations
Ds[charge(p)]= 1
lone-pair-electron (p) +
−P no-of-bond(p)
charge(p) −
+P lone-pair-electron (p) (a)
Individual-View “Charged-Nucleophile”
Individuals
p ;a piece-of-stuff e.g. Cl- or OH- Quantity-Conditions
charges(p, negative) Am[lone-pair-electron (p)] >= ONE
(b)
Individual-View “Delta-Minus”
(e.g. the “O” that bonds to “C” in the main chain) Individuals
p ;is a piece of stuff, the leaving group Preconditions
electronegativity(p) > electronegativity(carbon) Quantity-Conditions
leaving-group(p, good) Relations
Ds[charge(p)]= -1
lone-pair-electron(p) −
+P no-of-bond(p)
charge(p) +
−P lone-pair-electron(p)
(c)
Figure 3.6 (a) Generic definition for a nucleophile described using QPT (b) A nucleophile used in “make-bond” process (c) A “delta-minus” view. It is used when the covalent bond between a delta-plus and a delta-minus species is deleted.
3.6.3 Relation Between View Pairs and Organic Processes
In this work, a view pair is defined as having two individual views, in the form of
<Individual-View-1, Individual-View-2>. A view pair is used as the means to select
(and activate) a chemical process. In this thesis, a chemical process means an organic
process. The term “organic process” is used interchangeably to the term “organic
reaction” since organic reactions are modelled as QPT processes. The view pairs and
their associated organic processes will be stored as basic facts in the chemical
knowledge base. Sample cases are given in Table 3.1. Such results are obtained through
a detailed analysis performed on numerous reaction cases and the result has also been
verified with the chemists. The first row in Table 3.1 says: “a bond will be made (or
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formed) when the view pair of <neutral nucleophile, charged electrophile> exists”.
Likewise, a bond will be deleted when one of these view pairs exists: <neutral
electrophile, neutral nucleophile> or <neutral electrophile, charged nucleophile>.
Table 3.1: Relationship between view pair and covalent bonding.
No. Individual-View 1 Individual-View 2 Covalent
Bonding
1 neutral nucleophile charged electrophile make bond 2 charged electrophile charged nucleophile make bond 3 neutral electrophile charged nucleophile make bond 4 neutral electrophile charged nucleophile break bond 5 neutral nucleophile (delta minus) neutral electrophile (delta plus) make bond 6 neutral nucleophile (delta minus) neutral electrophile (delta plus) break bond
In Table 3.1, individual views in No. 3 and No. 4 are the same, but the covalent bonding
that will take place is different. So, which one would the software advise to occur?
The solution is to use the OntoRM ontology to disambiguate the situation (OntoRM will
be discussed in Chapter 5). We have presented the following two view pairs: <carbon,
−OH2+> and <carbon, HO−> in equation 3.2 and equation 3.4 respectively. Both view
pairs consist of a neutral electrophile and a charged nucleophile. But once they are
checked with the chemical knowledge, and if the two reacting units come from the same
compound then “deleting a bond” is the process that should occur. The OntoRM will
also check whether the carbocation is a stable one (in this case it is tertiary, so the
answer is yes). On the other hand, if the charged nucleophile is the one approaching the
compound then the reasoning engine will suggest the process of “adding a bond”
instead. By doing so, the nucleophile will substitute the leaving group. This is the
phenomenon of expulsion of the leaving group.
Let us examine another example. In the same table, individual views in No. 5 and No. 6
are having the same pair of views <delta-minus, delta-plus>, but the covalent bonding
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that may take place can either be “adding a bond” or “deleting a bond”. The reasoning
engine resolves this situation by looking at the structural unit of the compound to see if
there is still a charged atom around, if so then it will suggest to break the bond (e.g. the
lone pair electrons on the oxygen atom in “H2O” are donated to the hydrogen atom in
the substrate “(CH3)3COH2+”; hence a stable compound “(CH3)3COH” is formed.
Otherwise the reasoning engine will recommend that a bond should be added.
3.7 Reaction Steps Classified as “make-bond” and “break-bond” Processes
To build a model, it is necessary to identify relevant entities (views), properties and
relationships. As such the properties of the views were first studied and then moved to
study general characteristics by examining the states change of these views (or reacting
species) along the reaction route from initial state until the entire simulation ends. The
attempt was to assemble general properties and behaviour patterns of the reacting
species and the associated organic processes (covalent bonding) needed. This is our
technique towards automating the views and QPT processes construction. In our earlier
study (Tang and Syed Mustapha, 2006), we hand-instantiated QPT models for
representing the chemical theories of “make-bond” and “break-bond” processes, and
these models can be used in many organic reactions simulation. From examining a
number of chemical equations, one conclusion that can be drawn is that all organic
reactions needed to simulate the behaviour of the reactions defined in the scope of this
work will require only nucleophiles and electrophiles (both charged and neutral types).
Further investigation showed that the different chemical processes involved in a
reaction can be placed under either one of the “adding a bond” and “deleting a bond”
activities (the proof of this claim is presented in Section 3.7.1). The two chemical
bonding activities are named as “make-bond” and “break-bond” respectively.
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Thereafter, the two terms (“make-bond” and “break-bond”) will be used to refer to
adding a bond and deleting a bond.
3.7.1 Proof of Common Behaviour Exhibited in Organic Processes
As shown in Section 3.7, regardless of the names of the chemical processes, so long as a
process takes a view pair (consisting a nucleophile and an electrophile), then they are
either a “make-bond” or a “break-bond” process. In this work, the identified generic
processes are two, namely, “make-bond” and “break-bond”. This subsection details the
proof of our claim that the chemical properties of the two organic processes are easier to
be defined (as compared to the inorganic counterpart), therefore easing the construction
of the generic processes (represented as QPT models) to support task level reasoning.
Examples of task level processes are: “protonation”, “dissociation”, “halogenations”,
and so forth. Even though the names of the chemical processes are different, they can
be grouped under either one of the two generic processes. All organic chemistry
processes that involve adding a covalent bond between two views will exhibit the same
chemical behaviour and the individuals in the views will undergo the same chemical
changes. The sequence of use of these processes is vital in suggesting a particular
reaction mechanism. It is also ascertained that both the SN1 and SN2 mechanisms will
just need two processes (“make-bond” and “break-bond”), as given in Table 3.2.
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Table 3.2: A summary of the covalent bonding needed by three chemical equations presented in this thesis.
Equation 3.2 Equation 3.3 Equation 3.4
First Reaction Step
Protonation: (CH3)3COH + H+
“make-bond” process
Dissociation: (CH3)3C–Br
“break-bond” process
Br (a halogen) is leaving: Br
[HO-----CH2CH3] “break-bond” process
Second Reaction Step
Dissociation: (CH3)3C–OH2
+
“break-bond” process
Reaction with water:
(CH3)3C+ + H2O
“make-bond” process
Nucleophile attacks: (HO− ---CH2CH3-----Br)
“make-bond” process
Third Reaction Step
Capturing of anion by cation: (CH3)3C
+ + Cl–
“make-bond” process
Fast acid-base reaction:
(CH3)3COH2
+ + H2O
“make-bond” process
--
Remarks
Mechanism used = SN1 (existence of carbocation intermediate)
Mechanism used = SN1
Mechanism used = SN2 (concerted process)
A summary of the results obtained from Table 3.2 are that:
• Equation 3.2 can be explained by the SN1 mechanism and the reaction steps
comprised of: “make-bond”, “break-bond” and another “make-bond”.
• Equation 3.3 can be explained by the SN1 mechanism and the reaction steps
comprised of: “break-bond”, “make-bond” and another “make-bond”.
• Equation 3.4 can be explained by the SN2 mechanism and the reaction steps
comprised of: “make-bond” and “break-bond”.
The above information is included in the chemical knowledge base. Such information
can be retrieved at any stage during a simulation task (e.g. before suggesting an organic
process to take place). Despite of the different names (e.g. protonation, capturing of
anion by cation, etc.) used in each reaction step, these reactions are either “make-bond”
or “break-bond” processes. In addition, for a particular organic process, regardless of
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the specific names of the view pairs (e.g. <proton, alcohol oxygen> or <carbocation,
halide ion>), all the reacting species will undergo specific chemical changes. To justify
this claim, three chemical equations are used to demonstrate the chemical behaviour
which is common to these two organic processes involving different view pairs.
3.7.1.1 Behaviour Generalization for “make-bond” Process
In this section, three chemical equations for properties generalization purposes are
analyzed. Results of behaviour generalization for the two chemical bonding activities
are tabulated in Table 3.3 – Table 3.6. We rewrite the three chemical equations, as
follows:
(CH3)3COH + HCl → (CH3)3CCl + H2O (SN1mechanism) (CH3)3CBr + H2O → (CH3) 3COH + HBr (SN1mechanism) HO− + CH3CH2Br → CH3CH2OH + Br− (SN2 mechanism)
Table 3.3: Reacting species and their chemical changes in the “protonation” process (“make-bond”) of Equation 3.2.
Nucleophile (O)
Before
After Remarks Electrophile (H+)
Before After Remarks
Charge Neutral Positive Unstable Charge Positive Neutral Stable No. of
covalent bond
2
3
More than what it
should have
No. of covalent bond
0
1
Not informative
Lone pair electrons
2
1
Have not reached
maximum pair
Lone pair electrons
0
0
No change and not
informative
Table 3.4: Reacting species and their chemical changes in the “capturing of halide anion by carbocation” process (“make-bond”) of Equation 3.2.
Nucleophile
(Cl-) Before After Remarks Electrophile
(C+) Before After Remarks
Charge Negative Neutral Stable Charge Positive Neutral Stable No. of
covalent bond 0 1 Not
informative No. of covalent
bond 3 4 Stable
Lone pair electrons
4 3 Stable Lone pair electrons
0 0 No change
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Table 3.5: Reacting species and their chemical changes in the “reacts with water” process (“make-bond”) of Equation 3.3 for the formation of alcohol.
Nucleophile
(the “O” in OH2)
Before After Remarks Electrophile
(C+) Before After Remarks
Charge Neutral Positive Unstable Charge Positive Neutral Stable No. of
covalent bond
2
3 More than
what it should have
No. of covalent bond
3
4
Stable
Lone pair electrons
2 1 Have not reached
maximum pair
Lone pair electrons
0 0 No change
Table 3.6: Reacting species and their chemical changes in the “nucleophile attacks” process (“make-bond”) of Equation 3.4 for the formation of ethanol.
Nucleophile (HO-)
Before After Remarks Electrophile (the “C” that bond to the bromine)
Before After Remarks
Charge Negative Neutral Stable Charge Positive
Neutral
Stable
No. of covalent bond
1 2 Stable No. of covalent bond
3
4 Stable
Lone pair electrons
3 2 Not informative
Lone pair electrons
0 0 No change
We will now show how the establishment of the set of functional dependencies for the
“make-bond” process is accomplished. When the numerical data in Table 3.3 – Table
3.6 are examined, the following quantities dependency and effect propagation can be
established (in QPT notations). Note that “Y +
−P X” is used instead of the original QPT
equivalence “Y α +
−Q X”. The symbol “P” will be used in the software and it means
“proportionality”. One of the reasons is that the “P” is more receptive than the “α”to
chemistry students. It is also our intention to avoid using additional symbols in the
software, so that the chemistry students do not have to learn new jargons which can be a
burden to them.
The numerical data in Table 3.3, Table 3.5 and Table 3.6 allow us to define the
following qualitative proportionalities:
86
lone-pair-electron(O) +
−P no-of-bond(O) …(a)
charge(O) −
+P lone-pair-electron (O) …(b)
lone-pair-electron (H+) P no-of-bond(H+) …(c)
charge(H+) +
−P no-of-bond(H+) …(d)
Likewise, the numerical values from Table 3.4, Table 3.5 and Table 3.6 also enable us
to define the following relationships:
lone-pair-electron (Cl-) +
−P no-of-bond(Cl-) …(e)
charge(Cl-) −
+P lone-pair-electron (Cl-) …(f)
lone-pair-electron (C+) P no-of-bond(C+) …(g)
charge(C+) +
−P no-of-bond(C+) ... (h)
Interpretation of the above is given here. In all cases, an increase in no-of-bond of the
nucleophile (e.g. O and Cl−) will cause a decrease in its lone-pair-electron ((a) and (e)).
This in turn will increase the charge of the affecting species either from neutral to
positive (increasing) or from negative to neutral (also increasing). Recall that the
quantity space designed for charge is [negative, neutral, positive]. Notice that the
charge on electrophile is neutral after the “make-bond” process in each case (shown in
(d) and (h)). The chemical properties and reaction behaviour of the nucleophile in
Table 3.3 and the nucleophiles in Table 3.5 and Table 3.6 are the same while the
electrophiles in Table 3.4 and Table 3.5 share similar behaviour to that in Table 3.3.
This result confirms that the same set of chemical properties for modelling the Relation
slot of a QPT model for “make-bond” process can be reused. This means, in a “make-
bond” process, if the individual is an electrophile (or nucleophile) the same set of
general properties can be applied. For example, regardless of whether it is C+ or H+, so
long as it is an electrophile then it will demonstrate the same chemical properties
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change. Suppose that a “make-bond” process is determined (based on the view-pair
concept mentioned earlier), the required properties will be retrieved and composed to
give the required QPT model. Similarly, we can get hold of the general properties for
the “break-bond” process. In this work, these set of qualitative proportionalities are
stored in knowledge base as chemical theories (in the form of
qprop(X,Y,SignX,SignY)), where “qprop” stands for “qualitative proportionality”. For
example, charge(p) +
−P no-of-bond(p) is represented as qprop(no-of-bond, charge, plus,
minus).
3.7.1.2 Behaviour Generalization for “break-bond” Process
Table 3.7 tabulates the changes in chemical parametric values for the species in the
“dissociation” process of equation 3.2 for the production of alkyl halide. The
“(CH3)3COH2+” is viewed as having two reacting units (hence individuals). Here, “C”
and “O+” are modelled as the two individual views even though they are from the same
compound. Table 3.8 shows the states and values of the reacting species in the
“dissociation” process of equation 3.3 for the production of alcohol. Table 3.9 gives the
chemical data changes for the reacting species that are involved in the “X is leaving”
process of equation 3.4 for the production of ethanol.
Table 3.7: The reacting species involved in this “break-bond” process are “C” from the alkyl group and the “O” from the oxonium ion. The carbon is δ+, so that the electrons are pushed towards “O” which is more electronegative.
O+
| Before After Remarks C
Before After Remarks
Charge Positive Neutral Stable Charge Neutral Positive Unstable No. of
covalent bond 3
2 Stable No. of covalent bond
4 3 Unstable
Lone pair electrons
1 2 Stable Lone pair electrons
0 0 Not informative
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Table 3.8: In this “break-bond” process, the atoms involved are “C” and “Br” from the same molecule.
Br Before After Remarks C Before After Remarks
Charge Neutral Negative Unstable & reactive
Charge Neutral Positive Unstable
No. of covalent bond
1 0 Not informative
No. of covalent bond
4 3 Unstable
Lone pair electrons
3 4 Not informative
Lone pair electrons
0 0 No change
Table 3.9: In this “break-bond” process, the atoms involved are “C” and “Br”. Bromine is more electronegative than the other hygrogen substituents. So, it is the Br that leaves the molecule.
Br Before After Remarks C Before After Remarks
Charge Neutral Negative Unstable & reactive
Charge Neutral Positive Unstable
No. of covalent bond
1 0 Not informative
No. of covalent bond
4 3 Unstable
Lone pair electrons
3 4 Not informative
Lone pair electrons
0 0 No change
Functional dependency and effect propagation that can be derived from the above three
tables are as follows. From Table 3.7, the following functional dependencies are
obtained:
lone-pair-electron(O+) −
+P no-of-bond(O+) …… (i)
charge(O+) +
−P lone-pair-electron (O+) …… (j)
lone-pair-electron (C) P no-of-bond(C) …… (k)
charge(C) −
+P no-of-bond(C) …… (l)
Reactions in Table 3.8 and Table 3.9 exhibit the same behaviour as shown below:
lone-pair-electron (Br) −
+P no-of-bond(Br) …… (m)
charge(Br) +
−P lone-pair-electron (Br) …… (n)
lone-pair-electron (C) P no-of-bond(C) …… (o)
charge(C) −
+P no-of-bond(C) …… (p)
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The explanation is given as follows. The δ− species that pulls in electrons during the
“break-bond” process will get back one pair of unshared electrons (shown in (i) and
(m)) and further causing its charge to decrease ((j) & (n)). On the other hand, the
species that looses electron will increase its charge ((l) & (p)).
The above derivation of how the modelling decision is obtained has also been verified
and accepted by the domain experts. Based on the general properties, it is more
apparent how the various slots in Figure 3.7 (a “make-bond” specification in QPT) are
defined. The model can be used to reproduce the chemical behaviour of the first
reaction step of equation 3.2. In other words, when reasoning is applied to a QPT
model, the behaviour of adding a bond (or deleting a bond) can be reproduced.
On the other hand, the QPT model for “break-bond” process used in this work is
presented in Figure 3.8. The “break-bond” model is used to simulate the chemical
behaviour of the second step of equation 3.2. The QPT models were conceptually
validated by two chemists. The chemists concluded that the representation of chemical
theories in the models is acceptable.
Process Slots Neutral Nucleophile
(e.g. O)
Charged Electrophile
(e.g. H+)
Pre-Conds Am [no-of-bond(O)] = TWO is_reactive((CH3) 3COH) leaving_group(OH, poor)
--
Qty-Conds Am[lone-pair-electron(O)] >= ONE charges(O, neutral) nucleophile(O, neutral)
charges(H, positive) electrophile(H, charged)
Qualitative
Proportionality lone-pair-electron(O)
+
−P no-of-bond(O)
charge(O) −
+P lone-pair-electron(O)
lone-pair-electron(H) P no-of-bond(H)
charge(H) +
−P no-of-bond(H)
Direct
Influences
I + (no-of-bond(O), Am[bond-activity])
I + (no-of-bond(H), Am[bond-activity])
Figure 3.7 An instantiated “make-bond” process described using QPT modelling constructs. The process focuses on the nucelophile (the “OH”) to be replaced and the proton.
90
Process Slots Delta-Minus (δδδδ-)
(e.g. O+)
Neutral Electrophile (δδδδ+)
(e.g. C)
Pre-Conds bond-between(C, O) electronegativity(O) > electronegativity(C)
Qty-Conds Am[no-of-bond(O)] > Am[max-bond-allowed(O)] charges(O, positive)
--
Qualitative
Proportionality lone-pair-electron(O) −
+P no-of-bond(O)
charge(O) +
−P lone-pair-electron(O)
lone-pair-electron(C) P no-of-bond(C)
charge(C) −
+P no-of-bond(C)
Direct
Influences
I − (no-of-bond(O), Am[bond-activity])
I − (no-of-bond(C), Am[bond-activity])
Figure 3.8 An instantiated “break-bond” process described using QPT modelling constructs. The process focuses on the leaving group and the electrophilic carbon centre.
The common set of parameter dependency statements presented in this section forms
the basis for the automation of QPT models. The modelling constructs of QPT is
suitable for representing an organic reaction in terms of the movements of electrons
around an organic compound. This type of micro-level description mimics the way a
chemist explains how a reaction takes place.
3.8 Representing Organic Chemistry Theories Using QPT Constructs
In qualitative reasoning research, even though the key role is played by qualitative
simulation, the first and foremost task is to construct a model. Without a model,
reasoning cannot be started. As such the construction of process model is required
before qualitative reasoning can be performed. Once the right organic process is
determined, the fixed set of qualitative proportionalities (presented in Section 3.7.1) is
retrieved from the chemical KB and the required QPT processes are constructed. Since
chemical process reasoning based on QPT ontology starts from the direct influence, the
choice of representing the QPT slots is discussed in the following subsections.
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3.8.1 Direct and Indirect Influences in Organic Reaction Simulation
The direct influences are only two, adding a bond to the organic compound and deleting
a bond from the compound. The former is the direct effect of a “make-bond” process
while the latter is the direct effect of a “break-bond” process. When a bond is added or
deleted, it will bring about other effects, as shown in the Relation-slot of the QPT
models (Figure 3.7 and Figure 3.8).
3.8.2 Postulating Limit Points
Postulating the existence of limit points is a challenge in this domain. In QPT
formalism, use of limit points is important since they are crucial for prediction. For
example, some physical phenomena occur when a quantity’s value is above or below a
limit point. Table 3.10 gives samples of the limit points used in this work.
Table 3.10: Quantity spaces and limit points for the three main quantities used in the framework.
Quantity Quantity space Limit points
Charge [negative, neutral, positive] Where, Negative means unstable Neutral means stable Positive means unstable
[negative, neutral, positive]
When the value of the “charge” is either negative or positive, then the current process will stop and the next process may begin.
Covalent bond [lessThan, enough, moreThan] Where, lessThan means unstable enough means stable moreThan means unstable
[lessThan, enough, moreThan]
When the value of the “covalent bond” is either lessThan or moreThan, then the current process will stop and the next process may begin.
Lone pair electrons
[lessPair, enough, extraPair] Where, lessPair means unstable enough means stable extraPair means unstable
[lessPair, enough, extraPair]
When the value of the “lone pair electrons” is either lessPair or extraPair, then the current process will stop and the next process may begin.
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In literature, quantity space is defined as having the set of <decrease, nil, increase> or
the set of <plus, zero, minus>. However, the chemistry students are more familiar with
the numerical data representation of an atom’s basic chemical property. As “stable” or
“unstable” are related to “how many”, so the result presentation is given in numeric
form. Since it is easier for the students to match the answer to the one in their mind,
numerical output will be shown on the Graphical User Interface (GUI) when interacting
with the software. In other words, the numerical data are just “output representation”
and not used as variables in the simulation. Since the ultimate goal of this work is to
help students understand the underlying subject, the decision of displaying numerical
outputs is considered as acceptable and valid. Moreover, there is no mismatch of
answers between the system generated and the one from the textbook, and such decision
imposes no extra cost in terms of the effort to learn a new formalism.
Owing to the specific requirement in this chemistry system, limit analysis (Forbus,
1984) is not used the way it should be. Instead, a (new) phenomenon will occur
whenever a limit point is reached (i.e. having the exact condition). In QPT, when a
limit point is reached, something will occur and will cause the current process to stop.
In this work, however, the nature of this problem domain suggests that the limit point
may not necessarily be passed (in order to terminate a process), as illustrated in the
following example. The quantity space and its limit analysis have been adapted to cope
with the “a-bit-weird” condition caused by the nature of the problem. So, the standard
concept of limit point is not fully implemented, but it is replaced by tracking the
qualitative states (and their associated numeric value equivalence) in the quantity space.
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Let us take no-of-bond as an example, and recall that the numerical data space for no-of-
bond is: bond = [0, 1, 2, 3, 4]. The qualitative states for no-of-bond are: bond =
[lessThan, enough, moreThan].
We now look at the oxygen atom (O) and the carbon atom (C) in the main chain of an
alcohol (e.g. the (CH3)3C−OH presented in equation 3.1 on page 65):
For “O”, its stable state = enough = 2 (in numeric). After “protonation”, the new value
is moreThan, which is a limit point, so the process stops. On the other hand, for “C”
atom, its stable state = enough = 4. After “dissociation”, the new value is lessThan,
which is also a limit point, so the process stops. The above two cases obey the use of
limit points.
As for the incoming nucleophile/electrophile and the leaving group, their values are
used to determine whether a reaction step in the entire simulation route should stop due
to violation of its quantity-condition, as manifested below: The proton (H+) has no
bond, when the “protonation” process occurred, the “H+” (which serves as an incoming
electrophile) binds to the oxygen atom. Now, the hydrogen has its covalent bond =
enough (not a limit point) = 1 (in numeric), so the quantity-condition of this process is
violated hence the protonation is deemed to stop. On another note, after the
“dissociation” process, the “O+” has its covalent bond = enough (not a limit point) = 2,
the process will still end since the quantity-condition has been violated. As for the
“Cl−”, after the “make-bond’ process, it is stable = enough (also not a limit point), and
since the quantity-condition is violated, the process is deemed to stop.
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3.8.3 The Quantities and Quantity Spaces
Characteristics of objects are represented by quantities (object’s properties). Some
important quantities and the associated quantities spaces (values of numbers) are
provided in Table 3.11. Only “changing” quantities are shown in the table and not the
physical quantities. Examples of physical quantities are atomic number and period-
number. When quantities are coupled with derivatives, quantity space analysis can be
performed. For example, at any time the charge of any atom is either negative or
neutral or positive. The value of a quantity will change from left to right or from right
to left depending on the signs of changing (-1, 0, 1). There should not be any jump or
skip of value. In this work, the Quantity Space Analyzer (QSA) is the module that
keeps track of all these changes. Reading from literature, QPT was developed with
continuous changes in the developer’s mind. When designing the quantity spaces for
use with the quantities during reasoning, we modified a bit the standard way of defining
the quantity spaces. The non-standard way of using it in this work is due to the specific
requirements found in the problem domain, and we shall explain it here. Applications
of QPT rely on the understanding of physical laws and their mathematical expression in
physical and engineering systems (Forbus, 1993). These laws are used to specify
criteria to select values in composing each variable’s quantity space, expressed as the
relevance principle by Forbus (1984) and in combining values of different variables.
However, the organic chemistry system we are dealing with here finds no equivalent
physical laws or mathematical formalisms that can be used to specify criteria for
composing each variable’s quantity space. As such we modified the convention used in
selecting the quantity spaces to implement our QPT-based system.
Qualitative simulation used in this work consists of two levels of stopping conditions
described as follows. The completion of each of the small reaction steps is caused by
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the updated states of some parameters (quantities) such as new chemical states no
longer matched to the entry conditions of the process (e.g. “H+” becomes “H”). In
Chapter 4 we will show that the overall simulation of a chemical equation will end
when there are no more view instances to be paired up. This situation also implies that
the substrate is in its most stable form.
Table 3.11: Examples of quantities and associated quantity spaces. Quantity Quantity Space Remarks
charges [negative, neutral, positive] • At any time the charge of any atom is either negative, neutral or positive.
lone-pair-electron [lessPair, enough, extraPair] • “enough” refers to the lone-pair in stable state.
no-of-bond [lessThan, enough, moreThan] • “enough” refers to the number of covalent bond in stable state.
bond-status [partial, complete] • Complete or incomplete octet status. Species with incomplete octet is still unstable, a tendency to react further.
bond-activity [break-bond , make-bond] • During a process, a bond is either being made or broken.
nucleophile-reactivity [charged, neutral] • Charged species is more reactive than a neutral one.
bond-type [single, double, triple] • Only single bond is considered at current stage of work.
reactivity [first-degree, second-degree, third-degree] • Make provision for checking the carbocation stability.
electro-negativity [low, high] • Index for comparing two species in the same compound for its electro-negativity level.
3.9 Useful Guidelines in Modelling Views for Organic Reactions
There are some useful tips during the modelling activity especially in the mapping of
the chemical properties to QPT primitives. For example, in inorganic chemistry, a
reaction takes place by dissolving the reactants to produce ions and these ions have the
similar chemical properties such as “an increase in concentration will result in the
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increase of product formation, etc.” but when organic compound is used as the
substrate, it is the structure of the compound that determines what reaction mechanism
to apply in the synthesis route. Even though it is the functional group that is responsible
for a reaction, the mechanism used is dependent on the carbon centre (in the main chain
of a molecule). The primary (1ο), secondary (2ο), tertiary (3ο), or quaternary (4ο)
nomenclature is used to define a carbon centre. One of the ways of determining
whether a carbon centre is primary (1ο), secondary (2ο), tertiary (3ο), or quaternary (4ο)
is to count the number of bonds which are not bonded to hydrogen (Patrick, 1997a;
Patrick, 2000). For instance, a 3ο (tertiary) carbon is stable when its functional group
(the “OH”) is being removed, so the SN1 mechanism can be used to produce alkyl
halide.
Since the structure of a compound is important and there are many structural units in a
given substrate, this suggests that more than one view is required, one for each unit (and
not one view per substrate as used in QALSIC), as illustrated in Figure 3.9. In specific,
Figure 3.9 shows alcohol substrates with different degrees of carbons and thus these
substrates will exhibit varying reactivity under SN1 mechanism. For example, in the 3ο
case, one view should be designed for the OH portion and one more view for the
“(CH3)3C–”. This is what we meant by “looking at structures” is needed. This is
consistent with the example shown earlier (page 75) that tert-butyloxonium ion
“(CH3)3COH2+” is modelled as two views: “(CH3)3C−”, the alkyl part of the substrate,
and “−OH2+”, the oxonium ion.
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1ο (Primary) 2ο (Secondary) 3ο (Tertiary) One carbon Two carbons Three carbons (Not reactive) (Most reactive) H H CH3 | | | CH3 − C − O−H CH3 − C − O−H CH3 − C − O−H | | | H CH3 CH3
View-1 View-2 View-1 View-2 View-1 View-2
Figure 3.9 Alcohol reactivity under SN1 mechanism.
Three challenges were faced during early part of the modelling work. First, knowledge
abstraction is difficult because chemical commonsense is required. Humans tend to
make a lot of assumption in their reasoning and the chemical intuition required to
suggest reaction mechanisms is largely dependent on the commonsense knowledge one
has. Second, the setting of inequality for the quantity-condition is challenging. Unlike
other physical systems, the modelling of reaction mechanisms is not a straightforward
task, in that it is difficult to write (differential) equations to establish relationships
among variables. For example one can easily establish equation F = m.a for “net-force
+
+P mass” and “acc +
−P mass”. Also, in ecology, “growth-rate +
+P recruitment” and
“growth-rate +
−P mortality” to represent the expression growth-rate = recruitment –
mortality (Salles et al., 1996; Salles and Bredeweg, 1997). This is also the case in the
description of a heat-flow process where it can easily be identified that there is a
difference between the source and the sink temperatures (source-temp > sink-temp or
source-temp – sink-temp > zero). However, this type of relationship is not clear in our
problem. Nevertheless, we have identified and used the most fundamental aspect of a
chemical reaction to trigger the series of steps in a reaction, namely the reacting species
should be in their unstable states such as incomplete octets (valences have not been
completed). Third, QPT was developed with continuous changes in mind. However,
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this organic chemistry domain is found to be very precise for a qualitative reasoning
application from the perspective of the use of qualitative constraints over the variables.
As mentioned earlier, the use of qualitative constraints over the variables with discrete
values is part of our implementation efforts to put on “chemistry clothing” in the
software. Nevertheless, as far as the simulated results are concerned, the organic
reaction simulation can still be appropriately handled using QPT.
3.10 Learning with Qualitative Models
Model inspection can help sharpen a learner’s reasoning ability in the way that the
learner has to think hard why the statements in each slot (of the model) are relevant or
negligible. Note that in the evaluation stage, the students were given a short lecture on
the meanings and purpose of each QPT slot. Then, they are expected to read the model
constructed by the simulator prototype. In this section we will show how a qualitative
model can help articulate ideas about a learning task and to improve a learner’s
reasoning ability. The model inspection activity is divided into a series of learning
tasks. All learning tasks are based on the “protonation” process as illustrated in Figure
3.10 (representing the behaviour of the first reaction step for equation 3.2 on page 68).
Readers may refer to Appendix D.8 for a computer generated QPT model (captured
from the model viewer interface).
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Process “Protonation” (e.g. ((CH3)3COH) is protonated by H+)
Individuals
; electrophile (charged)
1. H ;hydrogen ion
; nucleophile (neutral)
2. O ;alcohol oxygen has a lone-pair electrons Preconditions
3. Am [no-of-bond(O)] = TWO 4. is_reactive((CH3) 3COH) 5. leaving_group(OH, poor) Quantity-Conditions
6. Am[lone-pair-electron(O)] >= ONE
7. charges(H, positive) 8. electrophile(H, charged) 9. nucleophile(O, neutral) 10. charges(O, neutral) Relations
11. Ds[charge(H)]= -1 12. Ds[charge(O)]= 1
13. lone-pair-electron(O) +
−P no-of-bond(O)
14. charge(O) −
+P lone-pair-electron(O)
15. lone-pair-electron(H) P no-of-bond(H)
16. charge(H) +
−P no-of-bond(H)
Influences
17. I+ (no-of-bond(O), Am[bond-activity])
Figure 3.10 The QPT process specification that models the behaviour of a “make-bond” process.
3.10.1 Ontology Primitives as Explanation Facilitator
During a reaction simulation, several types of queries may be expected. From the
interview conducted during the domain knowledge acquisition stage, the most popular
questions the students would ask are:
• What are the reacting species (the “individuals” in QPT terms) used in the chemical
process that occurred? Refer to learning task 1 for the answer.
• What type of alcohol (the “views” in QPT term) involved? Refer to learning task 1
for the answer.
• What are the chemical facts and properties that are true even after a chemical
process has occurred? Refer to learning task 2 for the answer.
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• Will a covalent bond be added or deleted from the compound? Refer to learning
task 3 for the answer.
• What happens to the functional group? Refer to learning task 4 for the answer.
• Why did the process occur? Refer to learning task 5 for the answer.
• Why was the process stopped? Refer to learning task 6 for the answer.
In this section only the usage of the various modelling constructs of QPT for answering
some conceptual questions will be explained while Chapter 4 will discuss the approach
used in handling questions after the entire simulation has been performed (i.e. the
understanding of the entire reaction route). One typical question of the latter is, in what
sequence the processes are activated and why it behaves so?
3.10.2 Learning Activities Manifestation
When the general chemical theories of a reaction are modelled as a QPT process, a
number of learning tasks can be devised, as follows. Note that line numbers are based
on the enumeration used in Figure 3.10 and equation 3.2 ((CH3)3COH + HCl →
(CH3)3CCl + H2O).
• Learning task 1: Proton (H+) and alcohol oxygen (OH) are needed by equation 3.2
simulation. Learners would be able to find this by inspecting the “Individuals” slot.
Briefly, the slot says that, in order to begin the first step of the equation 3.2
simulation, a proton is needed which serves as an electrophile together with a species
which has a nucleophilic centre. In this case, the nucleophilic centre is the “O” from
the “OH” group (termed as alcohol oxygen) which has lone-pair electrons to be
donated. Line 1 and Line 2 show exactly the existence of hydrogen ion together with
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the “OH” functional group from the alcohol which help explain why the two
substances are required.
• Learning task 2: Lines 3, 4 and 5 collectively say that the number of covalent bonds
on “O” is two; “(CH3)3COH” is reactive and “OH” is a poor leaving group. These
are basic information of what chemical properties that will remain valid throughout a
reaction for the involved substances.
• Learning task 3: In Line 6, the inequality (lone-pair-electron >= ONE) for the “O”
(which is the nucleophilic centre of the alcohol substrate) says “there is at least one
lone pair of electrons to be donated to H+”. Lines 7 – 8 indicate that “H” is a charged
species and thus it will act as an electrophile in the reaction. As such a covalent
bond would be added to the compound (the alcohol substrate).
• Learning task 4: When the “make-bond” process begins, the “O” will have an extra
covalent bond while the “H” will be neutralized. Chemistry students can appreciate
such concept by examining the functional dependencies as defined in Lines 13–16.
• Learning task 5: The process occurred because the statements in quantity-
conditions (Lines 6 – 10) are satisfied, which states that “alcohol oxygen with at least
one lone pair of electrons is needed so that the electrons can be donated to the proton
in order to make a bond”.
• Learning task 6: Lines 13–16 manifest that when the process begins, the “O” will
have an extra covalent bond while “H” will be neutralized. When more covalent
bonds are made on “O”, its number of lone pair electrons will decrease via the
inverse qualitative proportionality. When the lone-pair-electron on “O” decreases
the charge on “O” will increase. These relationships explain how the “O” donates a
pair of electrons in order to form a bond. At this point of time, the quantity-
condition has been violated. Therefore, the process is deemed to stop.
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3.11 Conclusion
This chapter fulfilled four objectives. First, domain knowledge acquisition has been
performed. Second, the QPT ontology has been examined and applied to modelling the
general behaviour of organic reactions. Third, classification of chemical processes for a
variety of organic substrates (as defined in the scope) has been accomplished. Last, the
chapter demonstrated the logic behind model automation and its justification; through
analyzing many chemical equations to reach a generalization state. In particular, this
chapter has answered two research questions: (1) “How can QPT be used to model the
behaviour of organic reactions?”, and (2) “How can qualitative model construction be
automated?” Our investigation determines that there are two main processes needed,
namely “make-bond” and “break-bond” for the entire reaction mechanisms, specifically
for SN1 and SN2. In the modelling stage, the basic principles of organic reactions were
learned, and the mental model of a few domain experts were sought and studied. Then,
the possibility of representing the chemical theories in qualitative terms using QPT was
attempted. The QPT ontology allows representation of chemical process elements at
the finest level of granularity. In the following chapter we shall discuss how these QPT
models can be used to support prediction of final products for a pair of reactants, as well
as the explanation generation approaches.
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Chapter 4 Qualitative Simulation and Explanation Generation
4.1 Introduction
This chapter describes the work done in achieving the following three objectives as
stated in Chapter 1:
• To design the qualitative reasoning algorithm for reaction mechanism simulation.
• To find an easy (yet natural) way of generating explanation effectively, in order to
facilitate mastering of organic reaction concepst via the QPT-based reasoning.
• To automate causal graph (state graph) generation as a means to explain an organic
process phenomenon.
Qualitative simulation and explanation generation play crucial roles in qualitative
reasoning research. Simulation along with explanation and justification of simulated
result are the central questions of this work. The organization of this chapter is as
follows. Section 4.2 reviews the state of the art in qualitative simulation and
explanation in education. Section 4.3 gives a scenario for the simulation of organic
reactions through QPT-based reasoning. The qualitative simulation workflow and
organic reaction reasoning will first be presented. Then the simulation task in a step-
by-step manner will be discussed; from the identification of what chemical process to
activate until the production of the most stable outputs. Section 4.4 explains the
chemical behaviour of SN1 and SN2 mechanisms. Section 4.5 discusses the specific
simulation for reproducing the behaviour of SN1. Section 4.6 presents examples and
situations for QPT model reusability. A scenario to warrant the claim that our models
are reusable will also be provided. Qualitative explanation and the approaches used in
justifying a simulated result are manifested in Section 4.7, through the use of causal
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graphs for deriving explanation. In particular, we will show how the explanation
follows isomorphically from the underlying QPT-based reasoning. The justification is
presented in several output forms (formats) using the vocabulary of the QPT formalism.
Section 4.8 discusses the simulation results based on QPT reasoning. Section 4.9
concludes the chapter.
4.2 State of the Art in Qualitative Simulation and Explanation in Education
Qualitative simulation allows the modeller to explicitly represent and reason about an
ill-defined dynamic system (with imprecise or partial knowledge) using only an abstract
structural model. From qualitative simulation, a description of all possible qualitatively
distinct behaviours can be derived (Forbus, 1984; de Kleer and Brown, 1984; Kuipers,
1994). These techniques have been used for tasks such as design, monitoring, and
explanation. In the past twenty years, simulation has been an effective method used in
modelling real world processes and objects for analyzing subjects such as behaviour of
a system. One of the earliest examples of the use of qualitative simulation in education
is the Meteorology Tutor (Brown et al., 1973). Brown and his group continued their
work which resulted in the SOPHIE systems (SOPHIE I, II, and III), in which a learner
can perform experiments easily and safely and receive informed feedback for the
troubleshooting of electronic circuits through the artificial lab or reactive learning
environment (Brown and Burton, 1982). In SOPHIE III, a qualitative simulator was
incorporated in an attempt to move towards more humanlike reasoning and explanation
capabilities. A significant feature of the SOPHIE systems was the robust natural
language interface which can handle a broad range of queries from the users. Kuipers
developed QSIM, another qualitative simulation program. The ontology used is
constraint-based (Kuipers, 1986). The approach started with a set of constraints
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abstracted from a differential equation and proved that the QSIM algorithm is
guaranteed to produce a qualitative behaviour corresponding to any solution to the
original equation. His work also showed that any qualitative simulation algorithm will
sometimes produce spurious qualitative behaviours, such as answers which do not
correspond to any mechanism satisfying the given constraints. Kuipers (1993) stated
that special care must be taken in designing applications of qualitative causal reasoning
systems and in constructing and validating a knowledge base of mechanism
descriptions.
The Teachable Agents project at Vanderbilt University (Biswas et al., 2001) shows an
example of how qualitative modelling can be useful for students. The work extended
intelligent learning environments with teachable agents to enhance learning. Their
Betty’s Brain system uses qualitative representations expressed in concept maps to
foster learning. Their qualitative modelling framework uses qualitative mathematics,
with tables for composing discrete values to provide qualitative simulation. Basically,
the task they use is to “teach” Betty (software) by building concept maps so that Betty
can produce explanations (Leelawong et al., 2001).
The QPT framework that supports articulate knowledge representation is another
qualitative simulation and explanation example that has been employed in educational
software of various kinds. For example, in the form of the self-explanatory simulation
software which incorporates simulations of liquids cooling and evaporating from cups
of different materials to help students understand the principles of thermodynamics
(Forbus, 1993; Forbus, 1996a) and in the form of the CyclePad Articulate Virtual
Laboratory software which supports learning about thermodynamical engineering by
the design of thermodynamic cycles (Forbus et al., 1999).
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Another example of qualitative reasoning work is Garp3 (Bredeweg et al., 2007).
Garp3 is a new workbench that allows modellers to build, simulate and inspect
qualitative models. The system was developed by Bredeweg and his research group
based on a number of previous software (e.g. GARP, Garp2 and VisiGarp). The entire
framework includes a simulation engine with access to model fragments and results via
a command-line interface. Model building tools such as HOMER (Bessa Machado and
Bredeweg, 2002; Bessa Machado and Bredeweg, 2003) and MOBUM (Bessa Machado,
2004; Bessa Machado et al., 2005) are part of the framework. VisiGarp and WiziGarp
are two recent add-ons to the workbench. The former allows users to inspect qualitative
simulation models by interacting with automatically generated visualizations. The work
investigated how explanations of dynamic phenomena can be generated using
qualitative simulations. The potential of aggregation principles to reduce the complexity
of qualitative simulations has also been explored (Bouwer, 2005). The latter, WiziGarp
prototype, incorporates the aggregation mechanisms and expands the communicative
functions of VisiGarp.
Salles and his team have also presented work on qualitative simulation in interactive
learning environment (in ecology domain) especially on how to find the minimum set of
model fragments needed for simulating the behaviour of a system and to answer
adequately a specific question, and how to provide the system with fault models that
reflect common misconceptions (Salles, et al., 1997; Salles and Bredeweg, 1997; Salles
and Bredeweg, 2001, Salles, et al., 2006).
Hirashima et al. (1998) introduced their Error-Based Simulation (EBS) method to
visualize an erroneous equation in a mechanical problem. In their work, the EBS-
manager predicts qualitative behaviour of the EBS by using qualitative simulation and
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compares it with qualitative behaviour of a normal simulation. When a qualitative
difference is found, the EBS-manager judges that the EBS is effective for error
visualization. The EBS-manager also tries to find parameters by using comparative
analysis of which perturbation causes the qualitative differences between the EBS and
the normal situation. After deriving the sequence of qualitative states based on an
erroneous equation by QSIM (Kuipers, 1994), the EBS-manager derives the sequence
of qualitative directions corresponding to the sequence of qualitative states with
perturbation of a parameter by using DQ-analysis (Weld, 1988).
Explaining dynamic phenomena is particularly hard because it involves describing how
a system changes over time. The expertise required to generate explanation about the
behaviour includes knowledge about the system under study, knowledge of the entities
of interest and how they relate to each other and knowledge of the processes that apply
to the situation and how they change the state of the system. In Laraba (2006),
explanation was viewed as a problem solving process with its own reasoning and
knowledge. Laraba sees the acquisition of a new practice in a contextual graph
corresponds to the addition of actions and contextual elements justifying the addition of
the action(s), hence providing explanation for why it is done in such a way. In his
work, the explanation module for qualitative simulation is used for justifying any state
transition in the behaviour tree and for explaining why an expected behaviour is
missing. The work has been continued to explore the explanatory tool that makes use of
contextual knowledge and of contextual graphs for modeling agent activity. With
contexual knowledge and graphs, the generation of user-based explanation and real-
time explanation are possible (Laraba and Brezillon, 2009).
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According to Valley (1992), there are two types of explanation, system-based and
domain-based. The former describes what has happened during a consultation, for
example, which rules have been fired and which facts have been deduced. To generate
this kind of explanation, a trace of the consultation must be kept. Domain-based
explanations contain information about the domain knowledge and justify system-based
explanations. Our system supports both types of explanation. To achieve system-
based explanation, several data structures are maintained (presented in Chapter 5), so
that it can be retrieved, translated into a user-friendly layout and then presented to the
user. To achieve domain-based explanations, the domain knowledge is explicitly
represented via the QPT process notion. Therefore, QRiOM can explain not only the
reaction steps occurred but also the reasons for following these steps.
The work described in this thesis combines the strengths of these various approaches,
including the use of simulations for education, the use of qualitative models to simulate
system behaviour and generate causal explanations. As far as qualitative simulation is
concerned, our framework supports four main tasks, as follows:
• Modelling (Chapter 3): automatic modelling of domain knowledge (into QPT
models) to simulate organic reactions.
• Reasoning and simulation (this chapter): apply reasoning algorithms to the
constructed models to reproduce the behaviour of “make-bond” and “break-bond”
organic reactions.
• Predicting (this chapter): make prediction of the most probable outcomes (final
products).
• Explaining (this chapter): provide explanation and justification for a simulated result
by using the proposed organic mechanism.
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Figure 4.1 relates the three common terms (reasoning, simulation and explanation)
described in this chapter.
Figure 4.1 The use of qualitative reasoning, simulation and explanation within the context of this work.
4.3 Qualitative Simulation Scenario
There are many cognitive steps leading from a chemical reaction to chemical solution.
Understanding the cognitive steps is among the many difficulties chemistry students are
facing such as lacking the skills to analyze the steps and translate the reactions into the
forms that can be used to predict the final product in reasonable and justifiable ways.
This section will demonstrate how this problem can be addressed by the qualitative
reasoning approach.
Modelling organic processes for reproducing the behaviour of SN1 and SN2 requires the
inclusion of the equilibrium phenomena. In our context, equilibrium is achieved when
Reasoning about the behaviour of the model
1. Causal changes (that stem from
QPT process reasoning) 2. Qualitative states of all parameters 3. A piece of “history” of processes that
occurred in a simulation
Causal Reasoning (The study of the cause-effect
interaction among parameters)
Behaviour Prediction
Explanation
Qualitative Simulation
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all of the reacting species reach the so-called “complete valence” state. This also
serves as the stopping condition. On the other hand, if valency is incomplete, reasoning
on the next process will proceed.
4.3.1 An Overview of the Simulation Architecture
Figure 4.2 depicts the workflow of the QPT-based reasoning for reproducing the
behaviour of organic reactions as well as the two mechanisms (SN1 and SN2). The
detailed version of the qualitative reasoning framework is presented as a collection of
flowcharts in Appendix B. In our approach, the construction of the qualitative model is
automated by a simple pair of substrate and reagent (the inputs). The key is to
recognize the name of the functional group that is attached to the input substrate. When
the “input recognizer” identifies the type of the inputs, the nucleophilic and
electrophilic centres will be known. The identities can then be used for determining the
chemical bonding activity (add or delete a bond). After that, a candidate organic
process will be selected and activated. During reasoning, some intermediates are
produced (and some are converted to other molecules) and they are placed in the View
Instance Structure (VIS). So long as there are view pairs left in the VIS, a new process
will be initiated and the reasoning process is repeated. When the entire reaction has
ended, users may ask for an explanation on any aspect of the organic reactions.
The workflow of the QPT-based reasoning can be summarized as follows: Given a
reaction in the form “A (substrate) + B (reagent)”, the nucleophilic and electrophilic
centres will be identified resulting in the nucleophile(s) and electrophile(s) to be stored
in the VIS. These electrophiles/nucleophiles are called “individuals” in QPT. After
this, a suitable process is determined by the view-pair concept. A candidate process is
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the one that satisfies quantity-conditions and pre-conditions. Besides, individuals that
are needed by the process must also be available in the VIS. When a process is being
activated, qualitative reasoning will begin. The reasoning engine will keep track of the
values of the changing states of the quantities being affected, starting from the first
process until the entire reaction ends. A process will stop when the statements in its
quantity-condition slot are invalid. When a process has occurred, some individuals
may cease (become new individuals) and updating of the VIS is necessary. If the
process produces a stable product, it will be stored separately for future retrieval. If
there are still reactive units (charged species or species that still have not completed
their valences), the reasoning process will be repeated, acting on other process
instances. The entire reaction will end when there are no more views to be paired up.
When a reaction has ended, outputs will be displayed, together with all the
steps/processes that occurred to produce the outcomes. This task is rather
straightforward since all the processes that occurred are recorded and the changes made
to each individual (described in terms of the functional dependency among quantities)
are also recorded. If a user needs an explanation for the results or has a question
regarding the behaviour of a quantity, then the explanation module will be executed.
Using this architecture, for each reaction, four main outputs can be obtained. These are:
(1) The final products (2) The steps/processes occurred, (3) The mechanism used, and
(4) The various forms of explanation to justify the simulated results (e.g. the reaction
route to produce the outcome).
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Start
Select a pair of
<substrate, reagent>
Anymore
view pair?
Construct QPT model based on the
suggested chemical process
Process Reasoning
(Qualitative Simulation)
Stop
no
Generate
explanation
Write final product to special
purpose array
Recognize reacting units in the
substrate and reagent, and
construct view pairs
Suggest an organic process based
on the view pairs
Module 1
yes
Display the final
product and the
mechanism used
Module 2
Explanations are generated based on the
chemical theories represented in the
QPT’s design primitives such as the direct
(I+/I-) and indirect (Ps) influences
Figure 4.2 Workflow of the QPT-based reasoning.
This chapter discusses module 1 and module 2 of Figure 4.2. Since QPT does not
describe how the constructed models are used, we have to design the “how” in our
reasoning algorithm (presented in Chapter 5) for running the qualitative simulator. The
simulation of a chemical equation is accomplished by “reasoning” about the behaviour
of the models constructed for the generic processes. As such, the application of QPT
reasoning to one specific organic process will first be introduced (Section 4.3.2) then
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moved on to describe the complete reasoning route for one specific chemical equation
simulation (Section 4.3.3).
4.3.2 Reproducing Behaviour of Organic Reactions via QPT Reasoning
A QPT model (as shown in Figure 4.3) is used to describe a specific reasoning task of a
reaction that adds a covalent bond between a nucleophile and an electrophile (so, it is a
“make-bond” process). In the figure, you may read the right column as “If (A and B)
Then (C and D)”. In this case, C and D are qualitatively reasoned.
This process occurs when the individuals (a nucleophile and an electrophile) are present
in the VIS. It is the candidate process because the statements in quantity-conditions are
satisfied (Lines 3 – 7), which say “the process needs a proton and alcohol oxygen with
at least one lone pair electrons to be donated to the proton in order to make a bond”.
The notion of “processes” defined in (Forbus, 1984) is used, in which “processes” are
the main causes of change in a chemical system. We represent chemical changes as
starting from the direct influence which then propagates via indirect influences.
Influences contain statements that specify what can cause a quantity to change, through
direct influences imposed by the process (label C). As the process occurs, bond-activity
is a direct influence’s process quantity and it has a positive influence (I+) on the no-of-
bond, which is defined as two direct influence statements using the “I+/I-” notation of
the QPT, as shown in Line 8 and Line 9. Other propagation of effect is defined in
Relation-slot (label D). It is propagated via a set of qualitative proportionalities defined
in the QPT process model. In this case, the number of covalent bonds the “O”
possesses is directly influenced by the process. The oxygen’s lone pair electrons will
decrease when more covalent bonds are made on the “O” via the inverse qualitative
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proportionality defined in Line 12. Decreasing lone pair electron on the “O”, will cause
and increase in its charge (Line 13). This will make the “O” a positive charge species
hence it is unstable. When the “O” is protonated, it is no longer neutral (explained in
Line 13) thus violating the statement in the quantity-conditions slot. As presented in
Chapter 3, these are the standard set of changes caused by a “make-bond” process for
the pair of <neutral nucleophile, charged electrophile> views. But how will this change
affects re-evaluation of the quantity conditions? The Quantity Space Analyzer (QSA)
will update the initial values of the affected quantities. If the entry conditions (as
defined in B) are violated, then this process will stop. The design of the algorithm for
the QSA module will be discussed in Chapter 5.
Process Slots Modelling constructs in QPT
A
B
C
D
Individuals 1. H ;represents hydrogen
2. O ;represents the alcohol oxygen
Quantity-Conditions 3. Am[lone-pair-electron(O)] >= ONE 4. charges(H, positive) 5. electrophile(H, charged) 6. nucleophile(O, neutral) 7. charges(O, neutral)
Direct Influences 8. I + (no-of-bond(O), Am[bond-activity]) 9. I + (no-of-bond(H), Am[bond-activity])
Relations 10. DS [charge(H)] = -1 ;decreasing sign 11. DS [charge(O)] = 1 ;increasing sign 12. lone-pair-electron(O) +
−P no-of-bond(O)
13. charge(O) −
+P lone-pair-electron(O) 14. lone-pair-electron(H) P no-of-bond(H) 15. charge(H) +
−P no-of-bond(H)
Figure 4.3 A “make-bond” model fragment represented using QPT. This model fragment is used to reproduce the behaviour of the first reaction step for “(CH3)3C–OH + HCl” reaction.
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So far, “where” the reaction might occur can be predicted but not what sort of reaction
will occur. A “mechanism” is the story of how a reaction takes place. It tells us how
the starting materials (organic substrates) and reagents react together to give the final
product (Patrick, 1997b).
4.4 Chemical Behaviour of SN1 and SN2 Mechanisms
Organic mechanisms can be used as a means to facilitate the mastering and
understanding of the fundamental principles of organic chemistry. This work looks into
one particular type of organic mechanism called nucleophilic substitution.
Nucleophilic substitution of an alkyl halide involves the substitution of the halogen
atom with a different nucleophile. The halogen is lost as a halide ion. The presence of
a strongly electrophilic carbon centre makes alkyl halides susceptible to nucleophilic
attack whereby a nucleophile displaces the halogen as a nucleophilic halide ion.
Equation 4.1 will be used to explain the behaviour of nucleophilic substitution.
RX + Nu− → RNu + X− (4.1)
In the transition state for this process, the new bond from the incoming nucleophile is
partially formed and the C−X bond is partially broken. The hydroxide ion (OH−) is a
nucleophile and uses one of its lone pair of electrons to form a new bond to the
electrophilic carbon of the alkyl halide. At the same time the C−X bond breaks. Both
the electrons in that bond move onto the halogen to give it fourth lone pair of electrons
and a negative charge. Since the halide is electronegative, this charge can be stabilized,
thus the overall process is favoured. There are two types of mechanism for alkyl
halides – SN1 and SN2. The two mechanisms are explained in following subsections.
Initially this work only investigated SN1. Later on, however, we found that the
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automated processes can be used to also support the simulation of SN2 – hence model
reusability is achieved.
4.4.1 The SN1 Mechanism
Let us examine the reaction of the hydroxide ion (OH−) with a tertiary halide (Figure
4.4). The hydroxide ion is obtained from NaOH when it is dissociates. This reaction
gives an alcohol product. Nucleophilic substitution has taken place and the rate of
reaction depends only on the concentration of the alkyl halide. Since the reaction rate
only depends on the concentration of the alkyl halide, the mechanism is known as the
SN1 reaction.
CH3 CH3 | NaOH | Br C CH3 HO C CH3 | | CH3 CH3
Figure 4.4 Reaction between the hydroxide ion (OH−) and a tertiary halide.
SN1 is a two stage mechanism. In the above reaction, in stage 1, the C−Br bond breaks
first with both electrons moving onto the bromine. The carbocation is stabilized by the
electron-donating effect of the three alkyl groups as well as hyperconjugation. This is
the rate determining step. The production of carbocation as an intermediate is unique to
SN1. The remaining alkyl portion becomes a planar carbocation. Steric strain is also
relieved. In stage 2, the hydroxide oxygen has a negative charge and is therefore a
nucleophilic centre. A lone pair of electrons is used to form a bond to the electrophilic
centre of the carbocation. The tetrahedral centre is reformed.
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Due to the steric bulk of the alkyl substituents, it is very difficult for a nucleophile to
reach the electrophilic carbon centre of tertiary alkyl halides. The attacking nucleophile
is unable to reach the electrophilic carbon centre of the alkyl halide. Therefore, these
compounds should not react with nucleophiles. However, in practice they do. How
then could the carbon centre become more accessible? The answer is the carbon centre
is tetrahedral. If, however, the C−Br bond is broken, a flat carbocation is obtained.
When the C−Br breaks, both electrons in the bond move onto the bromine atom to give
a fourth lone pair of electrons on bromine. Therefore, bromine gains a negative charge.
However, bonds do not normally break without reason. There are a few driving forces
which make the process possible. One of these is steric. The other reason is electronic.
There are two electronic effects which are involved in stabilizing the intermediate
carbocation. One of these is the inductive effect. Alkyl groups can have an inductive
effect whereby they “push” electrons to a neighbouring centre. This electron donating
effect could be seen as encouraging the departure of the halide ion. More importantly,
the electron donating effect of the alkyl groups helps to stabilize the carbocation since
the inductive effect reduces and hence stabilizes the positive charge. An advantage is
that the ion is more stable when it is planar since all three alkyl groups are as far apart
from each other as possible. The carbon atom is now sp2 with an empty 2py orbital. In
the carbocation, they are further apart and there is less steric strain as a result. The
order of reactivity of alkyl halides under SN1 is that tertiary alkyl halides are more
reactive than secondary alkyl halides, with primary alkyl halides not reacting at all.
The stability of a carbocation is an important factor for reproducing the behaviour of the
SN1 mechanism. Our choice of including “stability” as an essential chemical parameter
in the KB is based on the conditions presented in Figure 4.5. Obviously, this
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information is needed in order to make a decision whether to break a bond thus
allowing a structural unit to leave the compound.
Increasing carbocation stability
H H C | | |
C−C+ C−C+ C−C+
| | | H C C
Primary (1o) Secondary (2o) Tertiary (3o) carbocation carbocation carbocation
Figure 4.5 The stability of various structures of a carbocation under SN1 mechanism.
In order to make things clearer, an organic chemistry reaction is explained from the
perspective of organic mechanism. Our illustration is based on the following reaction:
“ROH + HX → RX + H2O”. One of the specific equations derived from the general
equation is: “(CH3)3C−OH + HCl → (CH3)3C−Cl + H2O”. The mechanism needs to be
the one that can explain a reaction which involves a hydroxyl (OH) functional group
transformation. To chemists, when a tertiary alcohol is used as the substrate, and
hydrogen halide as the reagent to produce alkyl halide, the SN1 mechanism is
necessitated. In summary, if the following evidences are observed, then the mechanism
for “(CH3)3C−OH + HCl → (CH3)3C−Cl + H2O” is SN1:
• There is a tertiary alcohol, where the functional group (i.e. the “OH”) is a poor
leaving group.
• There is a halide ion (X−) that acts as the nucleophile. HX dissociates to form H+
and X− since strong mineral acids (HCl, HBr, HNO3, etc.) are fully ionized in
solution.
• Protonation is required, i.e. a “make-bond” process is first needed.
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• A “break-bond” process will follow, in order to break the bond between the carbon
atom (in the long chain) and the functional group. This is to make room for the
substitution of the nucleophile that is attached to the initial compound. This step will
produce a stable carbocation.
• A final “make-bond” process is needed to complete the entire reaction since the
nucleophile in the organic compound (substrate) has been substituted and a stable
output is produced.
Based on the above facts, the thought processes underlying the general approach used in
writing mechanisms can be explicitly stated. For example, the organic mechanism for
the reaction in Figure 4.6 can be illustrated as shown in Figure 4.7. The three steps in
Figure 4.7 are modelled as QPT processes (as discussed in Chapter 3).
HCl
OH Cl + H2O
Figure 4.6 A reaction that needs SN1.
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H+
HO..
..−
�
H..O
|
H
−+
�
+
(Lewis base) (3o Carbocation)
Step 1 Step 2
+ ..
::..−Cl
..:
..Cl + H2O
(Lewis acid) Step 3
Figure 4.7 The organic processes occurred in the order of “make-bond” (Step 1), “break-bond” (Step 2) and “make-bond” (Step 3). The reaction can be explained by the SN1 mechanism.
4.4.2 The SN2 Mechanism
The order of reactivity for alkyl halides under SN2 changes dramatically from that
observed in the SN1 reaction, such that primary and secondary alkyl halide can undergo
the SN2 mechanism, but tertiary halides can react only very slowly. Primary alkyl
halides undergo the SN2 reaction faster than secondary alkyl halides. Reaction between
methyl bromide and the hydroxide ion (HO−) is a simple example of SN2 reaction
involving a primary alkyl halide, as shown in equation 4.2. The hydroxide ion is
nucleophilic because it has negative charge. The negative charge is on the oxygen, so
this is the nucleophilic centre. There is a concerted process where the incoming
nucleophile forms a bond to the reaction centre at the same time as the C−Br bond is
broken. The transition state involves the incoming nucleophile approaching from one
side of the molecule and the outgoing halide departing from the other side.
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Br CH3 + HO− → HOCH3 + Br− (4.2)
Factors affecting SN1 versus SN2 are: (1) solvent, (2) nucleophilicity, and (3) leaving
group. These three factors are considered in the design of the OntoRM ontology
(Chapter 5).
4.5 Simulation Scenario for Reproducing the Behaviour of SN1
The organic mechanism tells us how bonds are formed and broken and in what order
things happen. How do chemists interpret the chemical equation: “(CH3)3COH + HCl
→ (CH3)3CCl + H2O”? They will propose SN1 mechanism to explain the production
of the alkyl halide (i.e. (CH3)3CCl). In which, in the first step, the alcohol oxygen (the
“O” from the “OH” group) is protonated. This indicates the “O” captures a proton (H+).
This reaction produces the OH2+ which is a good leaving group. Next step is the
cleavage of the link between “C” and “OH2+”. Once the link is broken, a stable tertiary
carbocation intermediate is produced. The production of this intermediate is unique to
SN1 for an alcohol as the starting material. The occurring factors are that, the “O” in
“(CH3)3C−OH2+” is unstable since there are three covalent bonds (valency for oxygen is
two). At the end of this step, it will produce a water molecule (H2O). In the last step,
the incoming nucleophile (Cl−) can bond to the carbocation to form a neutral and stable
final product. The reaction step occurs since the two views are reactive (charged
species).
Based on the behaviour of SN1 presented in Section 4.4.1 and the way chemists interpret
the chemical equation, the overall simulation works as follows. Initially, there are three
reacting species: a proton (charged electrophile), the chlorine ion (charged nucleophile)
and the alcohol substrate (neutral nucleophile). Based on the <charged electrophile,
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neutral nucleophile> view pair, the “make-bond” process (Figure 4.3) is activated in
order to simulate the chemical behaviour of the first reaction step for the chemical
equation “(CH3)3COH + HCl→ (CH3)3CCl + H2O”.
• Step 1: The simulation scenario for this reaction step has been presented in Section
4.3.2. The new quantity created by this “make-bond” process is the oxonium ion
(−OH2+) and it will be placed into the VIS. All values assigned to each individual
are retrieved from the quantity spaces that keep track of the current values of each
quantity and the direction of change.
• Step 2: At this point, the oxonium ion (Line 1) required by the dissociation process
(Figure 4.8) is available in the VIS. The simulation thus continues by switching to
the second reaction step. Note that only the nucleophilic centre (“O”) is shown
rather that the entire structural unit (the “−OH2+”). The two reacting units come
from the same compound. This process describes the cleavage of the carbon-oxygen
bond in tert-butyloxonium ion ((CH3)3C−OH2+) which is unstable since the “O” is
now charged and has three covalent bonds. Changes that propagate via qualitative
proportionalities are as follows: The acceptance of two electrons from the
dissociation process will neutralize the “O” in “OH2+” (Line 7 and Line 8), hence
the water molecule is formed (and it will be kept in the side product array). At this
point, both the conditions in quantity-conditions slot are no longer valid. On the
other hand, the deletion of a covalent bond between “C” and “O” will affect its
charge, in that “C” is now positive charge (Line 10).
• Step 3: The carbocation ((CH3)3C+) and the chlorine ion (Cl−) are now left in the
VIS and happen to be the two individuals that are required to activate the generic
“make-bond” process. The task specific name for this bond making process is
called “Capturing of carbocation by anion”. In this third step, the individuals are
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“Cl−” (charged nucleophile) and “C+” (charged electrophile). The functional
dependencies defined in the Relations slot of Figure 4.3 can be reused (since both
processes are “make-bond”). Only the contents in the “Individuals” slot need
modification, where the two new individuals for this process are “C+” and “Cl−”
(refer to Figure 4.9). The start of this process can be explained by the incomplete
octets of a carbocation and a chloride ion and it stops due to the production of most
stable species where both ions complete their valences. Lines 9–12 in Figure 4.9
describe the following scenario: The increasing covalent bond on “Cl−” (Line 9)
propagates its effect to bring about the reduction of the number of its lone pair
electrons and further affecting the changing sign of its charge (from negative to
neutral, i.e. it is increasing). As for the other reacting species, the process’s quantity
(i.e. bond-activity) necessitates an increase in the number of covalent bond on “C”.
Here, “C” has regained its maximum bonds when its value is checked against the
simple facts stored in the knowledge base. The entire simulation ends here, as
chlorine and carbon atoms are both in neutral state.
Process Slots Modelling constructs in QPT
A
B
C
D
Individuals 1. O+ ( oxonium ion – functions like a delta minus) 2. C (carbon – functions like a delta plus)
Quantity-Conditions 3. Am[no-of-bond(O)] > Am[max-bond-allowed(O)] 4. charges(O, positive)
Direct Influences 5. I - (no-of-bond(O), Am[bond-activity])
6. I - (no-of-bond(C), Am[bond-activity])
Relations 7. lone-pair-electron(O) −
+P no-of-bond(O)
8. charge(O) +
−P lone-pair-electron(O) 9. lone-pair-electron(C) P no-of-bond(C) 10. charge(C) −
+P no-of-bond(C)
Figure 4.8 A “break-bond” model fragment represented using QPT. This model fragment is used to reproduce the behaviour of the second step of “(CH3)3COH + HCl”.
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Process Slots Modelling constructs in QPT
A
B
C
D
Individuals 1. Cl− (the incoming charged nucleophile) 2. C+ (the charged electrophile)
Quantity-
Conditions
3. Am[no-of-bond(C)] < Am[max-bond-allowed(C)] 4. Am[lone-pair-electron(Cl)] > Am[max-lone-pair-electron(Cl)] 5. charges(C, positive) 6. charges(Cl, negative)
Direct Influences 7. I + (no-of-bond(Cl), Am[bond-activity]) 8. I + (no-of-bond(C), Am[bond-activity])
Relations 9. lone-pair-electron(Cl) +
−P no-of-bond(Cl)
10. charge(Cl) −
+P lone-pair-electron(Cl) 11. lone-pair-electron(C) P no-of-bond(C) 12. charge(C) +
−P no-of-bond(C)
Figure 4.9 A “make-bond” model fragment represented using QPT. This model fragment is used to reproduce the behaviour of the third step of “(CH3)3COH + HCl”.
Now, the VIS is left with one species and the entire reaction is completed. The final
products are alkyl chloride ((CH3)3CCl) and water (H2O) which are very stable. The
sequence of process activation is “Protonation, Dissociation, followed by Capturing of
carbocation by chloride ion”. These three steps (reaction route) can be used to explain
the overall chemical change that occurred. At the end of the simulation, when checked
with the chemical KB, the sequence of processes occurred explains that it is in fact the
SN1 mechanism that makes the production of the final output possible. With this, the
“(CH3)3COH + HCl → (CH3)3CCl + H2O” is successfully simulated following the
behaviour of the SN1 mechanism.
4.5.1 Contents of the View Instance Structure (VIS) During Reasoning
During qualitative reasoning, the structures of the reacting species are updated and
recorded in VIS. Figure 4.10 depicts the running contents of the VIS during simulation
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of equation 3.2 while Figure 4.11 gives the contents in the VIS during the simulation of
equation 3.3. When a simulation is completed, the contents of the structure are
retrieved and these would be the final products of a reaction. Intermediate Structure
(IS) is used to store side products (if any). More internal structure representation and
molecular patterns stored as two-dimensional (2D) arrays are presented in Chapter 5.
(Initial state)
(a)
(After step 1) (b)
(After step 2)
(c)
(After step 3)
(d)
Intermediate
Structure
Figure 4.10 The contents in the VIS during the simulation of “protonation” process. The VIS is constantly updated to reflect the new intermediates produced until the entire reaction is ended. Content in (d) is the final product.
(Initial state)
(a)
(After step 1) (b)
(After step 2) (c)
(After step 3)
(d)
Intermediate Structure
Figure 4.11 The contents in the VIS during the simulation of the “dissociation” process. Content in (d) is the final product of this reaction.
4.5.2 Stopping Conditions for Reaction Steps and the Entire Simulation
A process will stop when the entry-condition is no longer valid. When the individual
instances required by the second process are available, simulation continues by
switching to the second step of the mechanism and finally reaches the step that produces
the most stable product. In this work, quantity-conditions only serve to start (or stop)
H2O
(CH3)3CO+HH (CH3)3COH
2H2O
(CH3)3C+
C+
2H2O
Cl-
(CH3)3C+ (CH)3CCl
Cl-
(CH3)3COH
H+ Cl-
(CH3)3CO+HH
(CH3)3CBr
H3O+
Br-
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the series of steps while the overall process is stopped due to only one species remains
in the VIS and the species must be in stable form.
4.6 QPT Process Model as Reusable Component
This section provides evidence to support our claim in Chapter 1 that the QPT models
constructed are reusable. The reusable components are the models developed for the
“make-bond” and “break-bond” processes. As described earlier, a reaction mechanism
describes the series of small changes that happened to a given pair of individual views.
These changes are caused by the occurring of either the “make-bond” or the “break-
bond” process (in a variety of sequence). Since organic processes will occur between a
pair of views such as: <charged nucleophile, neutral electrophile>, <neutral nucleophile,
charged electrophile>, <charged nucleophile, charged electrophile> and <charged
electrophile, neutral nucleophile>, hence the QPT models constructed for the organic
processes can support simulation of many chemical equations. The reaction as given by
equation 4.3 is used to illustrate this situation.
(CH3)3CX + H2O → (CH3)3COH + HX (4.3) Alkyl halide (in excess) Alcohol
Figure 4.12 depicts how bonds are formed and broken during the conversion of alkyl
halide to the alcohol product. The simulation of “(CH3)3CX + H2O → (CH3)3COH +
HX” is achieved by reasoning about the QPT models in the following order: “break-
bond”, “make-bond” and “make-bond”. The diagram also tells in what order these
processes happen. This is essentially the “mechanism” used in predicting and explaining
the result. The mechanism used is SN1 since it involves a tertiary alkyl halide and it is a
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unimolecular reaction where only the concentration of the alkyl halide will affect the
reaction rate.
(CH3)3C X
(a) The first reaction step is “break-bond” that disscociates the halogen atom from the molecule. The QPT model constructed for breaking the (CH3)3COH2
+ bond in Equation 3.2 is reused here.
..
(CH3)3C+ + : O – H
| H
(b) The second step is “make-bond” that bonds the electrophilic carbon centre to the nucleophilic oxygen centre of a water molecule. The QPT model constructed for making a bond between the alcohol oxygen of (CH3)3C–OH and a proton (H+) in Equation 3.2 is reused here.
.. .. (CH3)3C – O+ – H + : O – H | |
H H
(c) The third reaction step is also “make-bond”. The QPT model constructed for making a bond in Equation 3.2 can be used again.
Figure 4.12 The QPT process models constructed for Equation 3.2 can be reused by other chemical equation simulation such as Equation 4.3.
Even though the substrates used in both reactions (Figure 4.7 and Figure 4.12) are
different, the processes designed for Figure 4.7 can also be used by the reaction in
Figure 4.12. As one can see the two chemical equations can be explained by the same
mechanism, but both start with different substrates and produce different products. In
Section 4.6.1 (below), we will illustrate that the same QPT process models can be used
again for the SN2 mechanism.
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4.6.1 Model Reuse by SN2
Reusability of components is made possible by recognizing the individual small steps
required while each of the steps is determined by the individuals that are present in the
VIS. How does the SN2 reuse the QPT model? Figure 4.13 shows a reaction that
necessitates SN2. Even though the mechanism of this reaction is SN2, the models
developed for Figure 4.7 and Figure 4.12 can still be used. In principle, the two steps
are concerted, but it is modified so that the steps are executed in sequence (“break-
bond” followed by “make-bond”). Since the view pairs used to activate a QPT process
are different therefore different outcomes can be obtained. For example, the reaction in
Figure 4.13 uses the <delta-minus, delta-plus> pair for both its “break-bond” and
“make-bond” processes. Recall that in the simulation of Figure 4.7, a different pair of
views (i.e. <neutral nucleophile, charged electrophile>) is used to activate the “make-
bond” process. As long as it is a bond formation process, the earlier constructed “make-
bond” model can be used. Likewise, if it is determined to be a bond cleavage process,
the “break-bond” model is retrieved. The decision of letting the view pairs to determine
“when” a model is relevant addresses the “model selection” issue in QR research.
X
HO− + CH3X → [ HO
− CH3 ] → CH3OH + X
−
Figure 4.13 The mechanism used in this simulation is SN2. The organic processes that occurred are “break-bond” (expulsion of the leaving group) and “make-bond” (the approaching of the hydroxide ion to form a bond to the carbon centre).
129
The above description suggests that the reasoning order of the QPT models is vital in
the prediction of the final product(s) of a chemical reaction and in the reproduction of
behaviour of various organic mechanisms. The algorithm developed in the framework
can cater for such “select and sequence” ability, as shall be discussed in Chapter 5.
4.6.2 Model Reuse Scenario
The automated QPT models can be used to reproduce the behaviour of reactions such as
“A1 + B1”, “A2 + B2”, …, “An + Bn”. In other words, no matter what A’s and B’s are,
provided that they belong to the same class of nucleophiles and electrophiles, the same
make/break bond processes can be used (Figure 4.14).
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A1, A2, …, AN + B1, B2, …, BM
Input Recognizer
The constituents (Classified as either
nucleophile or electrophile)
Determine suitable process
Write to Reuse Qualitative of processes Modelling is done here for Processes Retrieve from
Qualitative Simulation
Predicted Results
View pairs and associated processes
Collection of substrates and their
constituents
Make-bond & Break-
bond Models
Figure 4.14 Model reuse scenario for the simulation of organic reactions.
It is the nature of the qualitative reasoning approach that supports the reusability of
models, in that it solves problem by having laid down the conceptual domain
knowledge rather than finding different factors to tackle each problem. The two
approaches of solving problems are depicted in Figure 4.15. Qualitative reasoning uses
the approach in part (a) of the diagram, in which “concepts” are akin to “processes” and
“problems” are akin to the different reaction mechanisms.
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Concepts
Problem1 Problem2 Problem3 Problem4
(a)
Problem
Factor-1 Factor-2 Factor-3 Factor-4
(b)
Figure 4.15 (a) A problem solving method that uses concepts to tackle multiple problems (b) A precoded KB of an expert system in solving a specific problem.
4.7 Qualitative Explanation Manifestation
Algorithms determine what the behaviour is, not an explanation of it. An explanation of
system behaviour may take many forms. An example is “causality” (causal accounts).
Causal account is a kind of explanation that is consistent with our intuitions of how
systems function. One of the objectives of this work is to prepare and generate
explanations in a language and format understandable to the learners and earlier we
solicited from the chemistry students that causal account is of help and meaningful to
them. Thus, causal graphs (state diagrams) are used to explain and justify solutions that
are returned by the system. Our approach stresses on the causal theories. Such
approach can generate explanation in a variety of forms, as will be discussed in Section
4.7.1 (generating a causal graph) and Section 4.7.3 (interpreting a causal graph).
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4.7.1 Generating a Causal Graph
The formalism of QPT which makes causality explicit is of great value in explaining
chemistry phenomena for teaching purposes. A chain of effect propagation represented
as functional dependency among chemical parameters will be constructed during
runtime by the QR algorithms (QSA module in specific, refer to Chapter 5 for
algorithms) that serve as an embedded intelligence module in the tool to produce the
causal graphs. A causal graph depicts the set of causal relationships between quantities
occurring in the simulation. One such cause-effect relationship is depicted in Figure
4.16 (a sketch for illustration purposes). The computer generated version can be found
in Appendix D.10 and Appendix D.11. The necessity of constructing such a graph is
first discussed while interpretation of the causal graph is provided in the following
subsection.
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Step 1: A "make-bond" process
H+ (Hydrogen ion, a charged electrophile) (CH3)3C-OH(Alcohol oxygen, a nucleophile)
no-of-bond(H) increased
charge(H) decreased
no-of-bond(O) increased
lone-pair-electron(O) decreased
charge(O) increased
The "make-bond" process
produces the (CH3)3COHH+
Step 2: A "break-bond" process
(CH3)3C(the C is an electrophile - delta plus) OHH+ (the O serves as a nucleophile - delta minus)
no-of-bond(C) decreased
charge(C) increased
no-of-bond(O) decreased
charge(O) decreased
lone-pair-electron(O) increased
The "break-bond" processproduces the carbocation
intermediate (CH3)3C+
Step 3: A "make-bond" process
Cl- (the chloride ion serves as a nucleophile) C+ (the carbocation serves as an electrophile)
charge(C) decreased
no-of-bond(C) increased no-of-bond(Cl) increased
lone-pair-electron(Cl) decreased
This is the last reaction step inthe simulation. It produces a
stable product (CH3)3CCl
charges(Cl) increased
a: Line 12
b: Line 13
c: Line 15
Figure 4.16 A causal graph showing cause-effect relationship of chemical parameters during the simulation of “(CH3)3C–OH + HCl” reaction.
For cross checking purposes, label “a” in Step 1 of Figure 4.16 represents Line 12
(lone-pair-electron(O) +
−P no-of-bond(O)) of the “make-bond” process depicted in
Figure 4.3. Similarly, from the same graph, label “b” is derived from Line 13
(charges(O) −
+P lone-pair-electron(O)) and label “c” represents Line 15
(charges(H) +
−P no-of-bond(H)) from the same model.
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4.7.2 Design of Causality
Basically, the behaviour of a chemistry system can be described as a sequence of
qualitative states occurring over a particular span of time. Our approach generates
explanation by examining the functional dependency statements in the QPT model. In
this work, apart from showing the qualitative state change of each atom involving the
conversion of the substrate to its final product, explanation is also supported by the
presentation of causal graphs. Causality can be used to manifest order upon the world.
For example, when given “X causes Y”, we believe that if we want to obtain Y we
would create X. As such, when we observe Y we will think that X might be the reason
for it. Qualitative proportionality (the P’s) helps propagate effects of change caused by
process quantity. For example, given two proportionalities (qp1 and qp2):
lone-pair-electron (O) +
−P no-of-bond(O) … qp1
charge(O) −
+P lone-pair-electron (O) … qp2
A question that can be asked (or derived) from the above two statements could be:
“How would the above qualitative proportionalities explain the “O” atom has positive
charge?” A causal explanation that could be generated is: The number of lone pair
electron will decrease when more covalent bonds are made on the “O” atom via the
inverse proportionality defined in qp1. In qp2, when the lone pair electron on “O”
decreases the charge on it will increase. The above functional dependencies can explain
why the charge on “O” is now positive. Simply, it donated electrons to form a covalent
bond. Another advantage of representing chemical behaviour using the qualitative
proportionality of QPT is that the state of the chemical system can be tracked over time.
Tracking of the state of a chemical system helps explain the underlying cause-effect
chain which is implicit in the qualitative models. The causal graph produced by QRiOM
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can be used to capture such dependency at runtime and then be used to generate
explanation on-the-fly (screenshots are presented in Chapter 5).
Figure 4.17 – Figure 4.19 are causal graphs that represent the minimal yet essential set
of properties abstracted from each reaction step presented in Figure 3.2 (page 68). Each
figure is discussed in turn. Figure 4.17 represents the cause-effect notion in the
“protonation” process. Legends used in these figures are: I = Influences and P =
Proportionalities.
lone-pair-electron(O) >= min-electron-pair(O)
protonation-activity
I+ I+ I-
no-of-bond(O) bond-activity(O) charge(H)
+
−P −
+P
lone-pair-electron(O) no-of-bond(H)
−
+P
charge(O)
Figure 4.17 Causal graph for the “protonation” process. The inequality above the dotted line is the entry condition to the process.
In Figure 4.17, the inequality statement shown above the dotted line represents the
quantity-condition that must be true for the protonation process to start. Effects are then
propagated via the direct (I) and indirect (P) influences. The protonation process
requires a proton (H+), from the HX acid. Since “H+” is electron poor, it will seek for
electrons. The other individual is “O” from the alcohol oxygen with the quantity lone-
pair-electron greater or equal to min-electron-pair in order to donate two electrons to H+
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to form a covalent bond between them. The process’s quantity is bond-activity. This
quantity directly influences no-of-bond for “O” and “H”. In other words, after the
protonation process “O” will have an extra covalent bond. The effects will propagate to
other dependent quantities shown in the diagram. For example, the number of lone pair
electron will decrease when more covalent bonds are made at “O” atom via the inverse
qualitative proportionality defined in qp3 (derived from the left branch of the graph in
Figure 4.17). In qp4, when the lone-pair-electron on “O” decreases, the charge on “O”
will increase. Besides, qp4 also explains why “O” is positive charge (loosing of electron
to make a covalent bond).
lone-pair-electron (O) +
−P no-of-bond(O) ... qp3
charge(O) −
+P lone-pair-electron (O) ... qp4
The dissociation behaviour is briefly shown in Figure 4.18. The process will start due
to instability of the oxonium ion (the extra covalent bond on the oxygen atom in the
oxonium ion). This situation is indicated in the inequality above the dotted line.
Changes that propagate via functional dependencies among quantities are: The charge
on “C” will turn positive and we shall get “C+” when one electron is transferred to the
oxonium ion to neutralize it and hence short of one covalent bond via the relation “no-
of-bond(C) +
−P charge(C)” (derived from the left branch of the graph in Figure 4.18).
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no-of-bond(O) > max-bond-allowed(O)
dissociation-activity
I+ I- I+
charge(C) no-of-bond(O) bond-activity(C, O)
+
−P +
−P
no-of-bond(C) lone-pair-electron(O)
+
−P
charge(O)
Figure 4.18 Causal graph for the “dissociation” process. The process stops when the oxygen (“O”) regains its equilibrium state.
Next, we shall examine the cause-effect interaction for the third process (Figure 4.19).
This process is caused by the incomplete octet of “C+” and “Cl−” and it will stop when
both the ions completed their valences.
no-of-bond(C) < max-bond-allowed(C) no-of-bond(Cl) < max-bond-allowed(Cl)
formation-activity
I+ I+ I+
no-of-bond(C) bond-activity(C, Cl) charge(Cl)
+
−P +
−P
charge(C) lone-pair-electron(Cl)
+
+P
no-of-bond(Cl)
Figure 4.19 Causal graph for the “Capturing of carbocation by anion” process.
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Capturing of carbocation by anion (or formation of alkyl halide) describes how the one
pair of non-bonded electron from “Cl−” combines with the “C+” to form a covalent bond
thus neutralizing both charges (refer to qp5, qp6 and qp7).
charge(C) +
−P no-of-bond (C) ... qp5
lone-pair-electron (Cl) +
−P charge(Cl) ... qp6
no-of-bond(Cl) −
+P lone-pair-electron (Cl) ... qp7
4.7.3 Interpreting a Causal Graph
Figure 4.16 depicts the overall chemical change of the substrate during simulation of
equation 3.2. We will now interpret the graph given that the building blocks have just
been discussed in Section 4.7.2. An organic reaction is triggered by an electrophile and
a nucleophile. Step 1 (“make-bond” process) is activated by the <H+, O> pair. In this
case, the nucleophile is the “O” from the OH group which has extra lone pair electrons
to be donated to the proton (H+, electrophile). The direct effect is that a covalent bond
will be made between “O” and “H”. These effects are propagated to other quantities as
follows. The charge on “H” is decreased (from positive to neutral) and the lone pair on
“O” is also decreased (donated to the electrophile). Decreasing the lone pair on “O”
will cause an increasing charge. The charge of oxygen atom is now turning from
neutral to positive. Assigning quantity values to each reacting species is coordinated by
the QSA module. All values that are assigned to each parameter are retrieved from the
quantity spaces maintained in the chemical KB. The first reaction step produces
(CH3)3COH2+ (an intermediate). From the chemical KB, this intermediate has “C” and
“O+” as the reacting species for a “break-bond” process to be activated. The “break-
bond” process (refer to Step 2 in Figure 4.16) describes the cleavage of the carbon-
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oxygen bond in tert-butyloxonium ion ((CH3)3COH2+) which is unstable since the “O”
is charged. The immediate cause of this process is that the bond between “O” and “C”
will break. State changes that propagate via functional dependencies among quantities
are: The acceptance of two electrons from the dissociation activity will neutralize the
“O” in “OH2+”. On the other hand, donation of electrons (lone pair decreases, as
indicated in the graph) will cause the charge on “C” becomes positive (charge
increases). This propagation produces a tertiary carbocation ((CH3)3C+) for the next
process activation use.
Atom “C” in the carbocation is now unstable and it is reactive. Since the carbocation
and the chlorine ion (Cl−) remain in the VIS, another “make-bond” process (“capturing
of carbocation by anion”) can be initiated. This is the third reaction step in the entire
simulation. The start of this process can be explained by the incomplete octet of the
carbocation and the chloride ion. The following describes Step 3: When a covalent
bond is made between “Cl−” and “C+”, the chlorine’s lone pair electrons will decrease.
This effect is further propagated to changing its charge (from negative to neutral state)
while the carbon’s charge is decreased (from positive to neutral). The entire process
ends here because both “Cl” and “C” are in neutral states (i.e. their valences having
been completed). Recall that, in this work, a process will stop when only one view pair
left in the VIS. From the graph, the final products are alkyl chloride which is very
stable and a side product (water molecule). The sequence of process activations are
protonation, dissociation, followed by capturing of carbocation by chloride ion. These
three steps can be used to explain the overall chemical change occurred. The
presentation of chemical effect propagation as a cause-effect diagram would help one
appreciate the general chemical principles underlying the chemical phenomenon and
hence be useful in improving one’s conceptual understanding in the subject.
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4.7.4 Deriving Explanation From a Causal Graph
The reasoning process which involves sequential changes of the substrate’s parameters
obeying chemistry theories will enable us to deduce how a particular chemical process
came about. A significant part of learning QPT models is that it could serve as
corrective feedback module in the entire learning endeavour. We will demonstrate how
the ontological modelling constructs of QPT can provide causal explanation about some
aspects of chemical system behaviour. Based on the causal graph in Figure 4.16, a set
of queries can be devised as shown in Table 4.1.
Table 4.1: A set of queries and explanations. The explanation is generated based on Step 1 in the causal graph presented in Figure 4.16.
Question Answer
How would the cause-effect relationships explain the charge on “O” is changed from neutral to positive?
The number of lone pair electrons will decrease when more covalent bonds are made on “O” and this effect is propagated to cause the charge on “−OH2
+” increases (from neutral to positive by referring to the quantity space).
Where did the electrons come from to form the new O−H bond?
From the extra lone pair electrons on “O”.
How would you explain a decrease in the lone pair electrons on “O”?
We know that the immediate cause of the process is the number of covalent bonds on “O” will increase (i.e. a bond is made). This quantity will influence the lone pair electron on it and the influence is strictly decreasing through the inverse proportionality relationship.
Why did the “make-bond” process occur?
The statements in quantity-conditions are satisfied, which briefly speak for “there needs a proton and alcohol oxygen with at least one pair of unshared electrons to be donated to the proton in order to make a bond”.
Why did the “make-bond” process stop?
When the process begins, the “O” will have an extra covalent bond while “H” will be neutralized. When more covalent bonds are made on “O”, its number of lone pair electrons will decrease via the inverse qualitative proportionality. When the lone-pair electrons of “O” decrease its charge will increase. These relationships explain how the “O” donated an electron in order to make a bond. At this point of time, the quantity-condition has been violated. Therefore, the process stops.
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Qualitative reasoning allows learners to access notions of how the behaviour of systems
evolves in time. The inspection of the cause-effect chain can help learners to develop
their chemical intuition so as to pick up the underlying concept better than merely
memorizing the reaction steps or basic facts. The explanation that is derived from the
qualitative model will engage students to rationalize why a particular process occurred,
and why was it stopped. This type of explanation is not precoded. To validate this, the
“dissociation” process is presented. Table 4.2 shows a few queries based on the “break-
bond” behaviour (refer to Step 2 in Figure 4.16).
Table 4.2: A set of queries and explanations. The explanation is generated based on the second step of the causal graph presented in Figure 4.16.
Question Answer
Why did the dissociation process occur?
The activation of the process is due to the extra number of bonds the alcohol oxygen possesses; where its covalent bond has exceeded the maximum number in stable state. Refer to the inequality: no-of-bond (O) > max-bond-allowed (O).
What are the factors that affect the reduction of covalent bond on “C”?
One of covalent bonds on the “C” will break and this is caused by the following factors. First, the dissociation process will directly influence the charge on “C” and this is strictly increasing (neutral to positive). Next, an increase in the charge will decrease the number of covalent bonds on the “C”.
What happened to the second lone pair of electrons on the “O” after this reaction step?
When the bond between the “O” and the “C” is broken, both electrons in the C−O bond move onto the oxygen to restore a second lone pair of electrons and thus neutralizing the charge and so that it could leave the organic compound as a neutral molecule called water.
Why was the dissociation process stopped?
This is because the entry-condition is no longer valid, i.e. the “O” has regained its stability.
4.8 Discussion
In this work, the time required for a reaction was not used as an influencing parameter.
This is because “time” does not affect the result. As such it was decided to include only
the essential chemical parameters for the entire modelling and simulation. The essential
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parameters are: (1) bonds (for changing the structure of a molecule), (2) lone pair
electrons, and (3) charge (for checking if an atom has filled up its valences). As far as
the simulation results are concerned, the simulated results matched those written in
textbooks. Note that only one round of simulation is implemented in software, in that a
simulation is considered complete when the nucleophile (or leaving group) in an
organic compound (serves as the substrate) has been replaced.
4.9 Conclusion
This chapter fulfilled three objectives. First, the use of QPT-based reasoning as the
simulation approach for organic reactions has been discussed. Second, a way of
generating explanation effectively via modelling constructs of the QPT has been
introduced. Third, the use of causal graph as a means to explain an organic process has
been presented. The procedures to generate and interpret causal graphs have also been
discussed. In particular, this chapter has answered two research questions: (1) “How
can qualitative reasoning be used to support organic reaction simulation?” (2) “How
can the modelling constructs of QPT be used to explain a chemical phenomenon?” In
this work, qualitative reasoning based on QPT ontology is used to predict the outcome
of “A + B”. The explanation and justification of a simulated result is achieved by
showing the “reaction mechanism” used. In this case, it is either the SN1 or SN2
mechanism. The suggested reaction mechanism will consist of the series of organic
processes used in the conversion of the reactants (“A + B”) to form the final product,
emphasizing on the chemical parameter states’ change that has occurred. QPT
reasoning supports behavioural explanation generation in order to facilitate mastering of
organic reaction concept. Explanation can be derived almost isomorphically from the
QPT constructs that are defined in the qualitative models. The explanation stresses on
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what is happening to the valence electrons in the molecule such as their movement and
rearrangement during a reaction. The reusability of models in supporting the
reproduction of the behaviour of SN1 and SN2 has also been demonstrated. We shall
discuss the entire qualitative reasoning framework in the following chapter.
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Chapter 5 Qualitative Reasoning Framework for Organic Reaction
Simulation
5.1 Introduction
This chapter presents the entire reasoning framework for the simulation of organic
reactions. The roles played by each component in the framework are presented. This
chapter also discusses the results of the following three objectives stated in Chapter 1:
• To develop a reasoning framework for organic reaction simulation and explanation.
• To define the types and roles of chemical knowledge at different abstraction levels
in order to facilitate effective use of the knowledge.
• To develop a small set of chemistry ontology called OntoRM for use with reaction
mechanisms for knowledge validation purposes.
The organization of this chapter is as follows: Section 5.2 gives the workflow of the
qualitative reasoning framework. A schematic view of the framework architecture
components and the roles of associated software modules are also presented. The
algorithm development for each functional component to be implemented in QRiOM is
presented and discussed in Section 5.3. The components encompass all the software
modules. These include the two-tier architecture for the knowledge base, the design of
data structures, substrate recognizer, model constructor for organic processes, reasoning
engine for reaction simulation, causal model generator and the design of attributes and
methods for an atom used in simulation. Section 5.4 discusses the method used for
storing organic compounds in software. Section 5.5 gives the design of structuring the
chemical knowledge in terms of their types and roles. Section 5.6 provides the protocol
for interacting with the software. The simulation results are presented and discussed in
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Section 5.7. Section 5.8 concludes the chapter with the fulfilment of the three
objectives mentioned above.
5.2 The Qualitative Reasoning Framework
The proposed framework uses QPT as the knowledge capture tool together with a set of
reasoning algorithms to systematically gather and reuse the chemical knowledge and
chemical theories to reproduce the behaviour of reaction mechanisms. The workflow of
the qualitative reasoning framework is presented as a collection of flowcharts in
Appendix B.1 – Appendix B.5. A schematic view of the reasoning framework is shown
in Figure 5.1.
5.2.1 Inputs
Inputs to the system are assumed to have no noise as the system only caters for alcohols
(primary, secondary and tertiary alcohols) and alkyl halides. This is because in our
present work, only the basic facts and theories related to alcohols and alkyl halides are
included in the chemical KB. The number of known organic compounds is more than
10 million. Only two families of organic substrates were selected as kick start inputs.
Nevertheless, more families of organic compound will be included in our full system.
Chemists deal with a variety of structures and transformation which can usually be
decomposed into clearly identifiable entities. Along this line, the organic compounds
are decomposed into the hydrocarbon chains (e.g. “CH3CH3CHC” and
“CH3CH3CH3C”) and the attachments (e.g. the functional group “OH”). Therefore, the
design of the input is rather straightforward, in that the organic compound and their
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decomposed units are stored as Prolog clauses. These basic facts will be retrieved and
populated on the Graphical User Interface (GUI).
5.2.2 Outputs
The simulator will return the following results: (1) final products, (2) intermediates
produced at each step, (3) sequence of processes used to reproduce the behaviour of the
proposed reaction mechanism, (4) overall structural change of the substrate, (5) QPT
model for organic processes, (6) causal graphs, (7) view pairs used in the simulation,
and (8) parameter state history for each atom that is involved in a reaction. Sample
results for (1) – (8) can be found in Appendix D.
5.2.3 Software Components
The reasoning framework consists of a number of software components. Figure 5.2
gives the main components – input recognizer, model constructor, reasoning engine,
explanation generator, knowledge validation module, OntoRM and chemical KB. Other
sub components are the Quantity Space Analyzer (QSA) and the Molecule Update
Routine (MUR).
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Substrate Recognizer
Module
QPT Model ConstructorModule
Qualitative SimulationModule
Final ProductsReaction Routes
Parameter Histories, etc.
Explanation Generator
Module
Substrate and Reagent
Selection
Modeling and Reasoning
Ou tp u t a n d Ex p la n a tio n
Input Module
Chemical
KB
QPTprocesses
OntoRM
ontology
Figure 5.1 A schematic view of the qualitative reasoning framework described in terms of the input, process, output and the knowledge bases.
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9
Knowledge
Validation
Routine
5
Explanation
Generator
3
Qualitative Simulator
(Reasoning engine)
Qualitative 2
Model
Constructor
Graphical User Interface 0
11
Chemical
Knowledge
Base
6
Causal Model
Generator
Substrate Recognizer 1
4
Simulated Results (Final products and the
mechanism used)
7
Molecule
Update
Routine
(MUR)
Molecule Patterns Storage
10
OntoRM
8
QPT
Process
Models
QSA
Various Types of Explanation
Figure 5.2 Main software components of QRiOM.
The software components depicted in Figure 5.2 are used in the following sequence.
Briefly, a substrate recognizer checks inputs entered by the user. Then, the qualitative
model constructor composes QPT models for organic reactions based on the identities
and types of the inputs. The reasoning engine is then called upon to simulate the
chemical behaviour using the constructed models. The explanation generator is called
upon when a user needs an explanation or a justification for a simulated result. The
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simulator will generate the following outputs and responses: causal diagrams,
qualitative models, predicted outcomes, suggested organic mechanisms, parameter state
histories, view instance structures (showing the use of reacting pairs in each reaction
step) and the entire reaction route of a simulation. Table 5.1 outlines the roles of each
module in the simulator.
Table 5.1: Main modules and their roles.
Module No. Roles
Module 0 (Graphical User
Interface)
• This module provides an interface for the learners to interact
with the system. • The module contains all the screen layouts and event driven
components.
Module 1 (Substrate Recognizer)
• This module checks user selection and returns the “type” of the input as either a nucleophile or an electrophile. From here on, an organic process may be determined.
• It also initializes a number of tables (E.g. 2D arrays) to hold the running results of various chemical parameters during simulation.
Module 2 (Model Constructor)
• This module automates the construction of QPT models based on the identity of user input.
• It will generate the QPT model as depicted in Appendix D.9.
Module 3 (Reasoning Engine)
• This module does the actual reasoning and final product prediction. This is where qualitative simulation takes place.
• The main reasoning functions are handled by the QSA and MUR.
Module 4 (Simulated Results)
• This module will return simulated results.
Module 5 (Explanation Generator)
• This module will generate explanation to justify a simulated result.
• It will retrieve various data structures (produced by the prediction engine) in order to generate explanation on-the-fly.
Module 6
(Causal Model Generator)
• Causal ordering (Iwasaki and Simon, 1986) is best known for dependencies and causality. So, the simulator constructs causal graphs to produce accounts of behaviour based on causality.
Module 7 (Molecule Update
Routine)
• This module keeps track of the structural change (pattern) of the substrate, from one organic reaction to another.
• It will display reaction route as shown in Appendix D.5 – Appendix D.7.
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Table 5.1, continued.
Module No. Roles
Module 8 (QPT Process Models)
Module 9 (Knowledge Validation
Routine)
• This data store contains qualitative models for organic processes.
• This is a routine called up by the reasoning engine whenever it needs to use a piece of knowledge to make a decision or to return an output.
Module 10
(OntoRM) • The reaction mechanism ontology that defines the basic
chemical knowledge and chemical commonsense for SN1 and SN2.
Module 11
(Chemical Knowledge Base)
• This data store contains information such as chemical facts and theories that are needed to perform qualitative reasoning.
The operational relationship of the various modules is as follows. Given a chemical
equation in the form of “A (substrate) + B (reagent)” (through the GUI, module 0), the
substrate recognizer (module 1) will check with the KB (module 11) to see whether it is
a valid input. If it is, then individual views are identified (module 2). Next, pairing of
views is carried out in order to construct QPT models (module 2 – qualitative
modelling) to prepare the chemical processes/reaction step. When there are active
processes, the reasoning engine (module 3) will keep track of the changing qualitative
states of the affected reactive units until the entire reaction ends. Along the reasoning
route, changes made to each individual’s parameters are recorded. This is handled by
the molecule update routine (module 7). The entire reaction will end when there are no
more reactive units. When a reaction ends, outputs will be displayed, together with all
the bonding and their sequence of execution (module 4). These steps can then be used
to explain the overall chemical change that occurred. If a user needs an explanation for
the results or has a question regarding the behaviour of a quantity, then the explanation
module (module 5 and module 6) will be run. Our approach for answer justification is
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based on cause-effect reasoning. The prediction engine calls up module 8 (knowledge
validation handler) as and when it is needed to disambiguate a situation and this task
relies on the OntoRM (module 9) to constrain the use of the knowledge. The main
modules and their associated use of knowledge are given in Table 5.2. Note that
“input” refers to the specific type of knowledge that serves as the required input to the
module while “output” refers to the new knowledge asserted to the knowledge base or
created as intermediate results.
Table 5.2: Software modules and the associated inputs and outputs.
Module No. Name of the input
Name of the output
Module 1 (Substrate
Recognizer)
1. The inputs (reactants selected by the user).
2. Prolog clauses that contain constituent parts of substrates.
The module produces:
• View pairs • Atom table • Initial View Structure
Array • Initial atom table • Bond table • Initial 2D molecule table
for the substrate
Module 2 (Model
Constructor)
1. Java classes of either “make-bond” or “break-bond” (depending on the view pairs).
The module produces:
• View and process models such as “make-bond” and “break-bond”
Module 3 (Reasoning
Engine)
1. Java class for the identified
covalent bonding and quantity spaces for relevant views are retrieved.
2. 2D molecule table, bond table, atom table and atom property table.
The module produces:
• Intermediate-product array • Updated View Structure
Array • Updated atom table • Updated 2D molecule table
and bond table
Module 4 (Simulated
Results)
1. OntoRM 2. Atom table 3. 2D molecule table 4. View structure array
The module produces:
• Name of the mechanism used
• Final output
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Table 5.2, continued.
Module No. Name of the input
Name of the output
Module 5 (Explanation
generator)
1. QPT models 2. Atom property table 3. View structure array 4. 2D molecule table
The module produces:
• Reaction route • Parameter history • Reacting units used in each
reaction step
Module 6 (Causal Model
Generator)
1. QPT models 2. Quantity spaces
The module produces:
• Causal graphs
Module 7 (Molecule
Update Routine)
1. Atom property table 2. Quantity spaces
The module produces:
• Bond table • 2D molecule table for the
changes made to the substrate
5.3 Component Design
The various components in the framework are implemented in software (the QRiOM
simulator). Code development is presented in Appendix E.
5.3.1 The Two-tier Architecture of Knowledge Base
The knowledge base has a two layer structure, as shown in Figure 5.3.
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Layer 2 (Upper layer): OntoRM
A chemistry ontology for reaction mechanism simulation represented as Java classes (It is accessed via QR algorithms; called upon during reasoning and result prediction)
Elements include:
List of possible end products
Processes that are allowed in nucleophilic
substitution reaction
List of possible order of processes execution
Layer 1 (Lower layer): Chemical Data Instances A pool of chemical facts and chemical theories coded in Prolog
(It provides basic facts of atoms and functional groups and their unchanged properties)
Elements include: Valence
electrons, covalent bond,
etc.
Nucleophilicity and carbocation
stability, etc.
Electro-negativity for nucleophiles and electrophiles
Qualitative proportionality for chemical
theories
Direct influences for
covalent bonding
Figure 5.3 Architectural design of the knowledge base.
The purpose of the lower layer is to provide basic facts for nucleophilic substitution use.
This layer is called “chemical instances” (or basic facts). Instances refer to chemical
elements and their chemical properties that do not change over time. Examples are
atomic weight, electro-negativity, valence electron and covalent bond (lowest normal
valence consistent with explicit bonds). The upper layer is the chemistry ontology for
reaction mechanisms simulation. This tier is called OntoRM; it is used as a tool to
define reaction mechanism in a formal way. The ontology defines the requirements and
constraints when suggesting a mechanism for a chemical equation simulation.
OntoRM provides only the “knowledge” and not how the knowledge is used. The
design of OntoRM is intentionally made to be “task neutral” in order to achieve two
objectives: (1) to conform to the definition of “ontology”, and (2) to promote ontology
reuse in other applications.
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5.3.2 The Chemical Knowledge Base
The QR algorithms will “process” the chemical parameters of reacting species involved
in a simulation. There is a lot of information required to support this task. Chemical
information to be processed is coded in Prolog. Prolog has a power feature called
“backtracking” that enables the program to use other alternative if the previous
alternative fails. This unique feature of Prolog will automatically choose the facts
needed to solve a query. In this work, the pool of Prolog clauses representing the basic
facts and theories of reacting species are termed as chemical knowledge base (KB). In
the development of the knowledge base, significant analyses are required in the problem
domain to decide the most needed (essential) chemical facts to be represented. Physical
quantities and chemical theories in the form of qualitative proportionality are also stored
in the chemical KB and not the reaction mechanism itself. New chemical facts can be
added to this KB since a GUI page is provided for the instructor to do so. Figure 5.4
shows an example of the chemical KB that supports the reasoning framework.
/*---- Chemical theories in Prolog syntax ----*/ qprop(make_bond, no_of_bond, lone-pair-electron, plus, minus). qprop(make_bond, charge, lone-pair-electron, minus, plus). // lower the energy higher its stability qprop(_, energy, stability, minus, plus). // less stable means more reactive qprop(_, stability, reactivity, minus, plus).
/*--- Chemical facts for reasoning use ---*/ covalent_bond('C', '4', stable). covalent_bond('O', '2', stable). lone_pair('O', '2', stable). lone_pair('C', '0', stable). charge(‘O’, neutral). charge(‘H+’, pos). charge(‘Br-‘, neg).
Figure 5.4 Examples of chemical facts and theories used in reaction simulation.
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5.3.3 OntoRM: Objectives and Motivations
OntoRM is an ontology defined specifically for the field of reaction mechanisms. The
concept of reaction mechanism includes the patterns of reactants and products and the
transformation operators (such as charge addition and subtraction, bond addition or
deletion). However, OntoRM is designed in such a way that the ontology does not
include any possible type of “final product” since the prediction of the final product is
handled by the suite of qualitative simulation algorithms. As shall be seen, only species
type (e.g. functional group) is included. There are no exact “patterns” for reactants
stored in the KB. Adding to that, the transformation operator is not fixed in the
definition of OntoRM. This is because the type of bonding is determined by the view
pair identification approach and as such OntoRM is used specifically for validating
whether the bonding is appropriate.
The representation format of OntoRM is frame-based. A “frame” consists of multiple
slots which are suitable for representing the attributes defined in OntoRM. OntoRM
guides the reasoning algorithm to make decision by validating and constraining the use
of chemical knowledge. In the implementation part, OntoRM is represented as a set of
Java classes with only attributes and no processing. Its main objectives are:
1. It is used to describe knowledge, requirements and constraints (if any).
2. It is used for defining and handling special cases.
3. It is used as a reference during validation (to validate uses of the KB) and to
constrain their use. For example, it is used to reject a decision during reasoning or
to confirm a prediction before returning the final products.
4. By using OntoRM, the use of chemical knowledge is made clearer and thus filled up
the gap in a previous work (as this was not found in QALSIC).
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The design of the ontology is motivated by the following factors:
1. QPT is a representation of domain knowledge in qualitative terms, with no
definition or description on how these knowledge are used (such as in what
order/sequence).
2. Previous systems stored all simple facts (elements and their properties) and other
unchanged chemical properties that can be obtained from the periodic table such as
atoms and their basic properties (e.g. oxygen’s maximum covalent bond is two
when it is in neutral state) with no emphasis on the structuring and hierarchical
organization of the domain knowledge. Therefore, a logical grouping of knowledge
is not found. With OntoRM, chemical data can be accessed and used effectively.
OntoRM can achieve the aim of component portability and reusability in other
applications. It is made very general in order to achieve portability so that it is
shareable by other applications. This is seen as a contribution to “computer in
chemistry” research.
5.3.3.1 The Design of OntoRM
Ontologies are normally used to abstract knowledge of a domain in a way that can be
used by both humans and computers by providing an explicit representation of the
entities of interest and the relationships among them (Dolan and Blake, 2009). One of
the earlier works on the design of chemistry ontology was described in Angele et al.
(2003). OntoNova is one such system developed under the Halo Project
(http://www.projecthalo.com) that answers questions from the “Advanced Placement
Test: Chemistry”. Encoding knowledge by experts appeared to be costly and it is just
one order of magnitude more costly than writing the natural language text itself. In
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OntoNova, basic concepts of chemistry are represented in F-Logic. Properties of these
concepts and relations between these concepts are represented by methods. Complex
chemical relationships and axioms are represented by rules. In this approach, a large
amount of specific cases are needed and it is again resorted to the traditional approach
of problem solving. An example of a rule written in OntoNova is as follows:
rule burnhydrocarbon: FORALL F,V1,V2,V3
burned(F):CombustionReaction[hasReactants->>{"O2",F};
hasProducts->>{"H2O","CO2"}] <-
burn(F) and hydrocarbon(F).
This rule states that if a formula F represents a hydrocarbon and is burned then the
reaction is identified as a combustion reaction with the reactants O2 and F and the
products H2O and CO2 of the reaction equation. The system relies on the OntoBroker
inference engine for solving equation balancing problems and generating explanation.
OntoBroker performs a mixture of forward and backward chaining based on the
dynamic filtering algorithm (Kifer and Lozinskii, 1986) to compute the subset of the
model for answering the query.
Hsu et al. (2006) developed ontology for patterns of molecule as well as reaction
mechanisms. The team has also developed a reaction network generator tool for
producing reaction networks based on the knowledge defined in their ontology. The
chemistry ontology defined by the research team consists of three different ontologies to
describe molecules/patterns, reaction mechanisms and reactions. The molecule/pattern
ontology defines elementary concepts such as atoms, bonds and patterns of atoms,
which are essential for describing any reaction, reaction mechanism or other chemical
phenomena. The reaction mechanism ontology defines concepts such as transformation
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operators that enable the representation of reaction mechanisms at an abstract level so
that the expert can rapidly test the hypothesis by simply combining several instances
stored in the knowledge base. Web Ontology Language (OWL) is used to encode the
ontology. In their work, a reaction mechanism, the input/output patterns and the
transformation steps are fully described. The reaction network generated unimolecular
and bimolecular elementary reactions following the exhaustive search process.
Our approach, on the other hand, has no pattern or output stored in any part of the
knowledge base or the OntoRM ontology. The outputs and molecule patterns are
predicted solely during runtime (by the qualitative simulator). OntoRM defines
chemical knowledge specifically for reaction mechanisms. The qualitative simulator
refers to this module to determine what aspects of the domain knowledge should be
presented to the simulator. In other words, the module performs only knowledge
validation, unlike OntoNova. There is no prior work associated with the design of
reaction mechanism ontology for validation use. As such, the use of reaction
mechanism ontology purely for validation purposes (rather than as an execution tool) is
considered as a new addition and contribution.
Another significance of the OntoRM ontology is that it can provide commonsense
knowledge to the qualitative simulator since the simulation of reaction mechanism
requires knowledge beyond what is maintained in the chemical KB. Besides, it can also
be used to disambiguate a situation. OntoRM defines the following entities:
• General definition for reaction mechanisms and functional units
• SN1 definition
• SN2 definition
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• Definition for Leaving Group (LG)
• Definition for Nucleophiles
• Definition for Electrophiles
The entities are arranged and organized as follows. Basic concepts of organic
mechanism are designed as hierarchy of IS-A (“is a”) relation. Properties of these
concepts are represented by Java classes having only attributes. It is likened to frame-
based representation scheme (slots consisting of parameters and data). Figure 5.5 –
Figure 5.7 define vocabularies that can be used to specify or determine a suitable
process. Table 5.3 gives the list of data types (together with the possible value sets)
used in defining the concept and properties of reaction mechanism while the
implementation formats are presented in Appendix E.
Functional Unit (FuncUnit) FuncUnit :: root Nucleophile :: FuncUnit Electrophile :: FuncUnit ChargedNu :: Nucleophile NeutralNu :: Nucleophile ChargedElec :: Electrophile NeutralElec :: Electrophile AlcoholOxygen :: NeutralNu ChlorideIon :: ChargedNu HydrogenIon :: ChargedElec Carbocation :: ChargedElec
(a) IS-A for “functional unit”
Reaction Mechanism (RM) RM :: root NucleophilicSubstitution :: ReactionMechanism Elimination :: ReactionMechanism ElectrophilicAddition :: ReactionMechanism Sn1 :: NucleophilicSubstitution Sn2 :: NucleophilicSubstitution
(b) IS-A for “reaction mechanism”
Figure 5.5 Basic concepts in OntoRM ontology are hierarchically structured using the IS-A relation.
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NucleophileView [ hasName => STRING; hasNeutral => BOOLEAN; hasCharge => BOOLEAN; hasBond => NUMBER; hasRsDegree => NUMBER; hasCarbocationStability => BOOLEAN; hasLonePair => NUMBER; hasReactivity => REACTIVITY_VAL; hasElectroNegativity => GREATER_LESSER; hasChargeOperator => PLUS_MINUS; hasBondOperator => ADD_REMOVE; hasNucleophilicity => NU_SCALE; ]
(a) Properties of nucleophiles
ElectrophileView [ hasName => STRING; hasNeutral => BOOLEAN; hasCharge => BOOLEAN; hasBond => NUMBER; hasRsDegree => NUMBER; hasCarbocationStability => BOOLEAN; hasLonePair => NUMBER; hasReactivity => REACTIVITY_VAL; hasElectroNegativity => GREATER_LESSER; hasChargeOperator => PLUS_MINUS; hasBondOperator => ADD_REMOVE; ]
(b) Properties of electrophiles Leaving_Group [ hasName => STRING; hasBond => NUMBER; hasBondType => BOND_TYPE; hasLonePair => NUMBER; hasDegreeSubstituent => DEGREES; hasElectroNegativity => GREATER_LESSER; hasNucleophilicity => NU_SCALE; hasAtomAttachmentType=>ATOM_ATTACH_TYPE;
hasReactivity => REACTIVITY_VAL; hasBaseStrength => BASE_STRENGTH; ]
(c) Properties of a leaving group
Substrate [ hasFunctionalGroupName => STRING; hasFunctionalGroupType => GRP_STRING; hasCarbonDegMainChain=>NUMBER; ]
(d) Properties of a substrate Alcohol [ hasName => STRING; hasBondType =>BOND_TYPE; hasReactivity => BOOLEAN; hasDegreeSubstituent => DEGREES; hasBaseStrength => BASE_STRENGTH; hasStability => BOOLEAN; hasLGType => LG_STRING; hasLGName => STRING; ]
(e) Properties of an alcohol
Alkyl_Halide [ hasName => STRING; hasBondType => BOND_TYPE; hasReactivity => BOOLEAN; hasDegreeSubstituent => DEGREES; hasBaseStrength => BASE_STRENGTH; hasStability => BOOLEAN; hasLGType => LG_STRING; hasLGName => STRING; ]
(f) Properties of an alkyl halide
Figure 5.6 Properties of basic concepts defined in the ontology are encapsulated in the format of a Java class.
ReactionMechanism Sn1 [ hasAlias => STRING; hasReactantNames => STRING; hasProduct => PROD_STRING; hasProductNames => STRING; hasDegreeSubstituent => NUMBER; hasReactivity => REACTIVITY_VAL; hasRateDetermineStep => WHAT_STEP_STR; hasReactionRateDependentNo => NUMBER; hasReactionRateDependentUnit => STRING; hasReactionRateDependentFactor => FACTOR_STR;
hasProcessOrder => PROCESS_ORDER_STR; hasViewsPairConstraint => SPECIES_TYPE; hasSpecialCause => SOLVENT_TYPE; hasAllowedDegreeOfCarbon => NUMBER; ]
(a) Sn1 reaction mechanism
ReactionMechanism Sn2 [ hasAlias => STRING; hasReactantNames => STRING; hasProduct => PROD_STRING; hasProductNames => STRING; hasDegreeSubstituent => NUMBER; hasReactivity => REACTIVITY_VAL; hasRateDetermineStep => WHAT_STEP_STR; hasReactionRateDependentNo => NUMBER; hasReactionRateDependentUnit => STRING; hasReactionRateDependentFactor => FACTOR_STR;
hasProcessOrder => PROCESS_ORDER_STR; hasViewsPairConstraint => SPECIES_TYPE; hasSpecialCause => SOLVENT_TYPE; hasAllowedDegreeOfCarbon => NUMBER; ]
(b) Sn2 reaction mechanism
Figure 5.7 Chemical properties of SN1 and SN2.
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Table 5.3: Data types and the associated values.
Data Type Values (Quantity Spaces)
ADD_REMOVE [add, remove] ATOM_ATTACH_TYPE E.g. “Carbon”, “Oxygen” BASE_STRENGTH [weak, strong] BOND_TYPE [single, double, triple, ring] BOOLEAN [yes, no] DEGREES [primary, secondary, tertiary] FACTOR_STR GREATER_LESSER
E.g. “concentration” [>, <]
GRP_STRING E.g. “Halide ion”, “Hydroxyl” LG_STRING E.g. “OH”, “Halide” NU_SCALE [low, high] NUMBER E.g. [1, 2, 3] PLUS_MINUS [+, -] PROCESS_ORDER_STR E.g. “make-bond, break-bond, make-bond”, etc. PROD_STRING [“Water”, “Alkyl Halide”, “Alcohol”] REACTIVITY_VAL [low, high] SPECIES_TYPE [“Neutral_Nucleophile + Charged_Electrophile”, “…”] SOLVENT_TYPE [temperature, nucleophilicity, pH] STRING E.g. “Chloride ion”, “Proton”, Hydroxide”, “Alcohol”, etc. WHAT_STEP_STR E.g. “The break bond process called dissociation is the...”, etc.
5.3.3.2 Validation Examples
This section demonstrates examples of how the OntoRM ontology can be used for
making sure that a correct piece of chemical data is passed to the simulator. What
needs to be validated? Earlier it was stated that only the “mechanisms” portion (e.g.
SN1, SN2, and the substrates) is considered. Why just reaction mechanisms? This is
because chemical processes and behaviour (e.g. proportionalities and direct influences)
are already handled by QPT. It serves also to constrain the use of some processes and
sequences that will not lead to a valid mechanism or final product so that wrong
reaction steps can be avoided. Basically, the semantic consistency for each small
reaction step in a proposed reaction mechanism is validated using the OntoRM.
Based on Figure 5.6 and Figure 5.7, the following validation examples are provided. In
each case, one of the definitions in OntoRM is retrieved:
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1. With regard to leaving groups, the reaction rate of both the SN1 and SN2 is increased
if the leaving group is a stable ion and a weak base. For example, iodide is a better
leaving group than bromide and bromide is a better leaving group than chloride (I >
Br > Cl > F). Also, alkyl fluorides do not undergo nucleophilic substitution. To
correctly make a prediction, the simulator needs to check the name of the leaving
group before carrying out the prediction. This information can also be included in
the pre-conditions slot of a QPT model such as to put “exclude fluorides for SN1 and
SN2 simulation”.
2. When (CH3)3CBr reacts with H+Cl−, there is a substitution of bromine by chloride,
but not in the case of (CH3)3CF and H+Br−, i.e., no reaction between the two species
(since fluoride is highest in its nucleophilicity). In the framework, the
“hasNucleophilicity” attribute is used to determine whether the simulation will
proceed or simply return the message: “no reaction since the X2 is less stable than
X1 when leaves the compound”. An example of the Java implementation of this
validation case is given in Figure E. 7 (Appendix E). This is another special case
example.
3. The “hasAllowedDegreeOfCarbon” in organic mechanism definition will check
whether an organic mechanism that is suggested by the simulator is acceptable.
This is possible since the attribute is linked to the allowable degree of carbon
attachment. For example, first degree carbon cannot undergo SN1. This can avoid
wrong simulation at the very beginning stage.
4. Alkyl groups release electron density better than the hydrogen, so the more alkyl
groups are attached to a positively charged carbon, the more stable the carbocation.
Such information (e.g. “hasStability” and “hasDegreeSubstituent” in alkyl_halide
definition) is included in the ontology to inform that “reaction can proceed if alkyl
groups are 2 or 3 degrees carbon”. Besides, the “hasDegreeSubstituent” field in the
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alcohol and alkyl halide definition can also be used to check whether a “break-
bond” process should be initiated (even though there is a suitable pair of views
available in the VIS). Such checking helps avoid producing unstable carbocation.
As an example, the system is expected to reject suggestion for deleting the bond
between the C and O of “CH3OH”, since this would result in a highly unstable
intermediate (the “CH3+” in this case). Only tertiary carbon is guaranteed in stable
state when the leaving group (LG) is departing from the main chain carbon. This is
achieved by referring to the “tertiary” value of “yes” for the “hasStability”.
Together, they guarantee the correct chemical process to be activated. An example
of the Java implementation for this case is given in Figure E. 9 (Appendix E).
5. The IS-A structuring of the reacting units can also help in pairing up the individual
views. Suppose that the <Br−, alcohol oxygen> pair is present in the VIS together
with the <Br−, C+> pair, there is no reaction for the former pair because both of
them are nucleophiles. If validation is performed, time is saved from predicting the
chemical changes that will not happen. An example of the Java implementation for
this particular case of validation is presented in Figure E. 8 (Appendix E).
6. In equation 3.2 (page 68), there are competing pairs of individual views to activate
different processes, such as between <C, alcohol oxygen> and <H+, alcohol
oxygen>. OntoRM can be used to resolve the situation. In this case, the
“hasBaseStrength” attribute in leaving group definition is needed, where “OH” is
found to have the “strong” value for its base strength. From our chemical KB, it can
also be found that “OH” is a poor leaving group, so it is not likely to break bond.
Otherwise the “OH−” will be very reactive thus violating the chemical principle of
moving towards a more stable state. However, the <H+, alcohol oxygen> pair has
no such restriction (since based on the result of the view pair identification module,
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it should be a “make-bond” process activation), so between the two candidate pairs,
the reasoning engine will suggest that the oxygen atom be protonated.
7. When a substrate is recognized as “alcohol”, the alcohol definition will be retrieved
and checked for its LG name and base strength. If the functional group to be
substituted is a “poor” leaving group, then a “make-bond” process will be
suggested. In OntoRM, the functional group “OH” (recognized through the parent’s
definition: “hasLGType”) possesses a chemical property of “hasBaseStrength”
which is determined to be “strong” (from the chemical KB), so the C−OH bond is
difficult to break. It has to first undergo a protonation process. If the checking is
not performed, the bond between C and O can break since C is a δ+ and O is a δ-
respectively. Surely, this is an incorrect step if allowed to proceed.
8. In the definition of the substrate, there is an attribute called
“hasFunctionalGroupType” that connects it to a list of valid functional group to be
used. This particular definition can be used to validate whether the initial compound
has completed a proper substitution of its nucleophile. For example, in Figure 3.3
(page 74), the “OH2+” that is attached to the main chain carbon is checked by
OntoRM. It was found that it is not a valid functional group, so it is not the final
product yet. Therefore, a further reaction step is recommended. After each reaction
simulation, the QR program will come to this part to check whether more reaction
steps are needed or the simulation is claimed completed.
9. The “hasElectroNegativity” can also help to determine, in a “break-bond” process,
the lone pair electrons on which atom is more available for donation. For instance,
when the C−Br bond breaks, a non-bonded electron pair on a less electronegative
atom (the C) is more available for donation than a non-bonded electron pair on a
more electronegative atom. Similarly, when the “C−OH2+” bond breaks, both
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electrons move onto the oxygen to restore a second lone pair of electrons and thus
neutralizing the charge. This is because the oxygen atom is a more electronegative
atom than the carbon atom therefore it has a greater share of electrons in the bond.
OntoRM helps structure and organize the chemical knowledge used by the simulator,
where proper use of chemical knowledge during simulation is achieved via the
knowledge validation module. Common ontologies typically specify only some of the
formal constraints that hold over objects in the input and output in the domain of
discourse. Commitment to a common ontology is a guarantee of consistency but not
completeness and there is no exception for OntoRM. This means that the ontology
cannot be used to validate all organic compounds in the domain of discourse, but
consistency can be achieved each time the vocabulary of OntoRM is used for validation
purposes.
5.3.4 The Substrate Recognizer
The inputs are represented as basic facts in the format of predicate logic (Prolog). Each
input has an internal structure that represents its decomposed units. This means there is
a corresponding decomposed array for each substrate. This is so because the chemical
process to be used is determined by the view pairs consisting of the decomposed units
(identified as nucleophiles and electrophiles). The decomposed units will also be placed
in the view structure arrays. Processing will proceed with these decomposed
substructures.
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5.3.5 The Model Constructor for Organic Processes
QR can work for a variety of domains and purposes. However, in the entire process,
“automatic model construction” is still a challenge, and it has not been satisfactorily
addressed this far. As mentioned by Bredeweg and Struss (2003),
“…automated model building, efficient algorithms and QR techniques to generate task-
oriented models from generic ones are among the QR research challenges…”
As explained earlier, this work constructs QPT models for processes from the set of
view pairs available. Such technique of automating model construction addresses one of
the issues of QR research – “model automation”.
QPT is used for modelling the domain knowledge. In particular, it is used to represent
the chemical theories that model the chemical intuition possessed by chemists when
solving organic reaction problems. Basically, a chemical process’s functional
characteristics are represented using QPT and its processing description is implemented
as a set of QR algorithms (since QPT does not describe how these models are used).
The top level design of the views and the process model automation steps is outlined in
Figure 5.8.
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INDIVIDUAL VIEWS AND PROCESSES MODELLING ALGORITHM
Qualitative_Modelling(substrate, reagent, QPT_MODEL) 1. Examine user inputs 1.1 Retrieve constituent parts of the substrate from chemical KB 2. Recognize structural units in substrates 2.1 Assign units as either nucleophile or electrophile
2.2 Store them in View Instance Structure (VIS) 3. Retrieve chemical facts and chemical properties of the nucleophile and electrophile 3.1 Compose the four slots of a QPT view
4. Suggest a chemical process based on the view pair 5. Retrieve the chemical facts and chemical theories of the suggested organic process
5.1 Assign process quantity to the direct-influence slot of the QPT model 5.2 Compose the other three slots: [Individuals, Quantity-Cond, Relations]
Figure 5.8 The main steps in the model constructor module.
5.3.6 The Reasoning Engine for Reaction Simulation
This is the heart of the framework where the mental model of a chemist is reproduced
during runtime by qualitative reasoning. The QR algorithm can mimic/simulate a
chemist’s way of reasoning when trying to propose a mechanism or to explain the
proposed mechanism. In the following subsections, the algorithm for the reasoning
engine is presented. The set of algorithms works well with the qualitative data (e.g.
chemical theories) represented in QPT. This is novel as far as chemistry software is
concerned.
Changes are caused by continuous chemical processes which provide the notion of
mechanism for causality. These changes propagate through the system that indicates
causal relationships between quantities. A set of algorithms to “use” the knowledge has
been developed. Figure 5.9 gives the main steps of the reasoning algorithm
implemented in QRiOM.
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QPT-BASED SIMULATION ALGORITHM Q_Simulation(QPT_model, OUTPUT) 1. Perform qualitative reasoning on the constructed QPT model 1.1 Store the process’s entry conditions 1.2 Store the directly influenced process quantity 1.3 Keep track of the state transition (handled by the QSA module) 2. IF process_stopping_condition = true THEN Store propagated effects in special purpose data structures Store new individuals in the VIS Update the VIS END_IF 3. Update the substrate’s molecular structure (handled by the MUR module) 4. IF VIS contains reactive individuals THEN Determine a suitable chemical process
Go to step 1 ELSE Retrieve final product from the VIS Call OntoRM to check for validity of the predicted product
Call OntoRM to check for the possible order of process execution Write the final product and the proposed mechanism to OUTPUT
END_IF 5. Return OUTPUT
Figure 5.9 The main steps of the simulation algorithm.
5.3.7 The Causal Model Generator
Explanation generation by software is never an easy or a straightforward
implementation. Nevertheless, when the behavioural aspect of the reaction problem is
described in qualitative terms, the “causality” concept is inherent in the model, where
functional dependencies can help explain the effects of propagation caused by a
process. Qualitative representation using the constructs of QPT provides us with a
simple means to capture the intuitive, especially causal aspects of human mental
models. The causal model presented in Figure 4.16 provides a medium for the students
to examine (and trace) graphically the dependency between chemical parameters. This
can help to scaffold the students’ reasoning ability. The inspection of cause-effect
chains can help a learner to pick up the underlying concept better than merely
memorizing the reaction steps or basic facts. QRiOM is able to provide this type of
explanation on demand. Appendix D.10 and Appendix D.11 show two screenshots of
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the causal graphs generated by QRiOM for explaining respectively the SN1 and SN2
mechanisms in producing the outcomes of reactions. The component that handles this
task is the QSA. Figure 5.10 presents the algorithm for QSA that constantly keeps track
of the state transition of a molecule in order to generate the causal graph on demand.
The essential steps and data needed to generate a causal graph are shown in Figure 5.11.
QUANTITY SPACE ANALYZER QA_Analyzer(quantity_name, initial states of affected quantities, quantity space)
1. Store initial states of each quantity in special purpose arrays 2. Perform qualitative arithmetic 2.1 Examine the sign and direction of change (derivative) of the quantities 2.2 Check the relevant quantity spaces for new values 2.3 Update qualitative states 2.4 Store propagated effects in special purpose data structures 3. Stop
Figure 5.10 Main steps in the QSA module.
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Figure 5.11 The flowchart for generating a causal graph.
5.4 Storing Molecular Patterns in Software
Chemists are pioneers in building electronic databases for storing chemical compounds.
The huge amount of chemical information is difficult to be handled manually by human
(Engle and Gasteiger, 2002). Therefore, chemists started quite early in storing
information in electronic form. Each year more than 6,000,000 new chemical
compounds are registered in the Chemical Abstract database
(http://www.cas.org/substance.html). In this work, chemical structures are represented
by “structure diagrams” which consist of the atoms of molecules and how these atoms
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are connected by chemical bonds. Such a representation is called a connection table.
Examples are shown in the following section.
5.4.1 Design of Attributes and Methods for an Atom
The properties of atoms need to be updated from one reaction step to another. Some
essential attributes and methods associated with an atom are given in Table 5.4.
Table 5.4: Some attributes and methods associated with an atom.
Attributes The role of the attribute during an organic reaction
simulation
Molecular structure The molecule to which the atom belongs.
Bond neighbours The list of bonds which are connected to the atom.
Atom neighbours The list of atoms which are only one bond away from the atom.
Own lone pair electrons This is the number of electrons which present in the valence shell and do not belong to a bond.
Own charge The charge of an atom is determined by its own valence electrons and by the number and order of the bond neighbours.
Own no. of covalent bonds
This is the number of links (bonds) that connects the atom to other neighbouring atoms. Only the atoms that are connected one bond away is counted.
Methods The main role played by the method associated with an atom
during an organic reaction simulation Connect to bond
Given a new bond that is to be attached to the atom, the method updates the list of bond neighbours and the lone pair attributes accordingly using the QPT indirect influence.
Connect to atom This method will create a new bond to form a link to another atom. In this work, it is either a nucleophile or an electrophile.
Update own charge, lone pair and covalent bond total
This method further checks and updates other dependent parameters such as the charge of both the atom in question and the approaching ones.
Disconnect from bond This method carries out the inverse procedure from the method that connects to a bond.
Disconnect from atom This method carries out the inverse procedure from the method that connects to an atom.
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In this work, the attributes and methods of atom and bond objects focus on representing
and modifying the connectivity of the molecular structure, in a way similar to the work
described in Mavrovouniotis and Forsythe Jr (1998). When a suitable chemical
bonding is determined, then the bonding process will be activated. Suppose it is a
“make-bond” process. The first task is to create a bond (through the “direct influence”
construct of the QPT) on the atom in question. The next task is to update the states of
other dependent chemical parameters, as well as the states of chemical parameters for
the incoming nucleophiles/electrophiles. The “bond neighbour” and “atom neighbour”
will be updated accordingly. It is plus one for the former attribute and the list of atoms
which is one link away will be updated to respond to the latter attribute. The extra atom
attachment will also be used to update the overall molecular structure for the organic
compound.
5.4.2 Connection Table
A connection table has a 2D structure representation. Figure 5.12 shows the computer
representation of the “C–O–H” structure.
Atoms Atom1 1
Atom2 2
Atom3 3
: AtomN
N
(a) The individual atoms in a substrate’s functional group
Atom1 Atom2 Atom3 .. AtomN Atom1 0 Atom2 0 Atom3 0
: 0 AtomN 0
(b) The connection table is presented as a 2D array
Figure 5.12 A substrate’s functional group represented as a connection table.
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Bond order is not considered in our work since only single bond species are used. A
specific example is given in Figure 5.13, where the initial bond connection for the
functional group of the alcohol is shown. In the reasoning framework, the table entry is
updated as soon as the reasoning is performed on the structure during the “make-bond”
process (“protonation” in this case), as shown in Figure 5.14. These tables can then be
used to update the 2D molecule table.
C O H Remarks C 0 1 0 “C” has 1 chemical bond with “O”. O 1 0 1 “O” forms 2 chemical bonds. H 0 1 0 “H” has 1 chemical bond which is connected to the “O”.
Figure 5.13 Connection table for initial structure of the substrate.
C O H1 H2 Remarks
C 0 1 0 0 - O 1 0 1 1 Three bonds on “O” is unstable
H1 0 1 0 0 - H2 0 1 0 0 -
Figure 5.14 Connection table after the “protonation” process (“make-bond”). The digit “1” is filled in the correct entry based on the individuals that activates the process. “H2” indicates the newly added atom.
5.4.3 The Molecule Table
The structure of the substrate is constantly being updated from one reaction step to
another until the entire simulation is finished. The changing of a compound’s molecular
structure is recorded in special purpose data structures. These structures can be
retrieved at a later step for explaining the proposed mechanism by showing the reaction
steps occurred. The component that handles this task is the MUR. Sample structures
will be shown in the following subsection. The output generated by the computer can
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be found in Appendix D.5 – Appendix D.7. The main steps in updating the whole
substrate are presented in Figure 5.15.
Molecule_Update_Routine (MUR) IF process_name = “make-bond” THEN Recognize incoming element and the index of the atom in the molecule to form a bond Add a bond to the atom to be attached to the incoming element Update the molecule_table Update charge and lone pair properties for both the affected atoms in atom_property_table Add the new element into row+1 and col+1 of the bond_table Insert “1” in the table entry of the newly inserted element in the bond_table ELSE_IF process_name = “break-bond” THEN
Recognize the position of the atom to be detached Remove a bond from the atom Store the removed element in VIS Update the molecule_table Update charge and lone pair properties for both the affected atoms in atom_property_table Remove all elements starting from the index of affected atom until lastRow/lastCol in the bond_table END_IF Figure 5.15 Algorithmic steps in the MUR module that updates the molecule table in order to prepare the reaction route of a chemical reaction.
Figure 5.16 shows the structure of the alcohol substrate before any reaction takes place
while the content of the molecule after the “H” has been added is shown in Figure 5.17
(observe the shaded entries):
1 2 3 4 5 6 7
1 CH3 2 | 3 CH3 C O H 4 | 5 CH3
Figure 5.16 A molecule table is represented as a 2D array. This is the initial structure of the alcohol substrate.
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1 2 3 4 5 6 7
1 CH3 H 2 | | 3 CH3 C O H
4 | 5 CH3
Figure 5.17 The “H” has been attached to the main compound. This is the effect of the generic “make-bond” process.
In Figure 5.17, to add a covalent bond to “O” (Column 5, Row 3), a link is formed at
(Column 5, Row 2) and an atom “H” is placed at (Column 5, Row 1). To remove a
covalent bond from “C” (Column 3, Row 3), the entries for “Column + 1” onwards will
be replaced by “ ” (blank). In other words, for each column, from 4 (column adjacent
to carbon) to 7 (last column), entries in rows from 1 to 5 are deleted.
A real challenge was faced in the design of the algorithms used for drawing the 2D
patterns for the substrates’ molecular structures. It was decided in the end to focus only
on the structural units (nucleophilic centre) that will undergo substitution. In this way,
most atoms (especially in the long chain of a compound) remain unaffected and there is
no need to carry out updating tasks on these atoms.
5.5 Knowledge Structuring
The intention of structuring the chemical knowledge used in the simulator is to
overcome what was lacking in QALSIC. The chemical KB is organized as having three
abstraction levels and four types. Each type of knowledge is stored in a different Prolog
file, so that whenever a set of related chemical facts or theories is needed, the correct
file will be retrieved. Besides, the order of use of the knowledge can be traced by
keeping a log of the different chemical KB file being used during qualitative simulation.
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Such a log file is useful when we want to find out “when” each type of the knowledge is
used in a particular reasoning route. As noted in Chapter 2, QALSIC has quite a
number of qualitative processes but how they are structured in the system for efficient
retrieval is not well-defined in the literature. To address this issue, the domain
knowledge is organized by grouping all chemical theories specific to an organic
reaction in the same data store. With this measure, fast retrieval for a set of related
chemical information for an identified chemical process can be achieved. Table 5.5
defines the meanings for the three levels of knowledge use in QRiOM while the four
knowledge types and their roles together with their knowledge levels are given in Table
5.6. The inclusion of knowledge typing and the different abstraction levels of
knowledge are motivated by the work described in Bredeweg (2001).
Table 5.5: Three abstraction levels of knowledge for use in QRiOM.
Abstraction
Level
Description
Domain
Knowledge
This level of knowledge is task neutral (i.e. “how” they are used is not
specified). The design is therefore looking for relations that can be
used under different organic reaction mechanisms. Examples are the
declarative facts and relations.
Inference
Knowledge
It specifies how the domain knowledge can be used for qualitative
reasoning. It points out the role the domain knowledge plays in the
reasoning process. This level of knowledge can be represented as a
model that describes the real world system in which the behaviour of
that system does not change during a period of time. Examples are
conceptual and conditional knowledge stored in the chemical KB that
are used for composing QPT models for organic processes.
Strategic
Knowledge
It controls the overall reasoning, e.g. how tasks can be selected for
achieving goals. The steps defined in the QR algorithms are considered
as strategic knowledge.
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Table 5.6: Knowledge types, abstraction levels and roles for use in QRiOM.
Knowledge
Type
Abstraction
Level
Roles Examples specifically designed
for organic reaction
mechanisms
Conceptual
Domain Knowledge
• They are not tied up with any execution
• They are stored in chemical KB as facts and relations
• reagent(‘HCl’) • reagent(‘HBr’) • nucleophile(‘Cl-‘, charged) • electrophile(‘H+’, charged) • is_a(‘OH’, leaving-group) • is_a(‘H’, proton) • product_formed(water,
‘H2O’, stable) • substrates(alcohol,
‘CH3OH’) • functional_unit(nucleophile) • functional_unit(electrophile)
Computational
Domain Knowledge Inference Knowledge
• Mainly used by the reasoning algorithm during simulation to compute and update the states of chemical parameters
• covalent_bond(‘C’, 4, stable) • covalent_bond(‘O’, 2,
stable) • covalent_bond(‘H’, 1, stable) • lone_pair(‘C’, 0, stable) • lone_pair(‘H’, 0, stable) • lone_pair(‘O’, 2, stable) • q_space(charge, ‘[neg,
neutral, pos]’).
Conditional
Strategic Knowledge
Inference Knowledge
• The knowledge used
in partial model for “when” to apply the model fragment
• E.g. those statements defined in quantity-
conditions slot of a QPT model
The following two statements: • electro-negativity(‘O’) >
electro-negativity(‘C’) • Am[no-of-bond(‘O’)] >
Am[max-bond-allowed(‘O’)] are represented in software, as follows: • electronegativity(‘O’, ‘>’,
‘C’) • greater(no_of_bond,
covalent_bond)
Additional
Strategic Knowledge Inference Knowledge
• The “pre-condition” used in organic process models
• This type of knowledge is normally used to disambiguate situations
• The definitions can also support special cases reasoning
• leaving_group(‘OH’, poor) • leaving_group(‘Cl’, good) • non_reactive_unit(‘H’,
‘CH4’) • nucleophilicity(charged, ‘>’,
neutral) • nucleophilicity(charged, ‘>’,
neutral) • stable_ion(‘Cl-’) • stable_ion(‘Br-’)
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5.6 The Protocol for Interacting with QRiOM
Implementing the qualitative reasoning framework is one of the objectives of this work.
In order to substantiate our claim that QPT models can be automated (described in
Chapter 3) and the qualitative reasoning framework is realistic, QRiOM has been
developed.
Figure 5.18 shows the problem solving model of QRiOM (i.e. the protocol to interact
with the software tool). The user can repeat any function as many times he/she desires.
The tool also provides explanation via special functions to emphasize certain chemical
theories and the general concept of a particular chemical process. In order to facilitate
user control over a simulation task, the navigational interface includes the following
functions:
• Moving forward and back one screen at a time within the same reaction simulation.
• A list of clickable buttons is provided that can be accessed in random sequence.
Such features enable users to compare the various forms of results generated at the
end of a simulation in the same text area.
• Multiple tables to display what has been changed in an atom from the start state
until the end of a simulation.
• Jumping to a particular explanation page.
• System exit can be done easily (from all the major GUI pages).
• Each button is annotated with brief function and uses. This enables users to read
ahead before a particular button is clicked.
• Some buttons are disabled to avoid wrong sequence of running a simulation. For
instance, model construction function needs to be run before a simulation can be
performed.
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• Even though users can click on any button that appears on an interface page, there is
always a button framed with green colour to draw the user attention. This
“coloured” button indicates it is the correct button to click.
• A periodic table is provided to check physical properties of an atom used in the
simulation.
• A glossary of chemistry and QPT terms (and jargon) is also included.
• A printer-friendly function is provided to send narrative notes to a printer.
Select substrate and
reagent
Build process model
(Automate QPT model
construction)
Run simulation
View final products and the
reaction mechanism used
Examine the entire
reaction route
Inspect qualitative model
(QPT models)
Analyze causal graphs in
explanation page
Study changes in atoms’
chemical parameters
Corresponds
to A
Corresponds
to B
Corresponds to
C, D & E
Corresponds
to F
Corresponds
to G
Corresponds
to H
Corresponds
to H
Figure 5.18 Protocol in using the simulator (Labels A – H can be found in Figure 5.19).
In this work, the tool is designed in such a way that the user is taken through the
modelling, simulation and explanation pages step-by-step. A computer screenshot of
the main interface of QRiOM is given in Figure 5.19.
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A
B
C
D
E
F G I H
Figure 5.19 Main interface of the QRiOM software.
The navigation is achieved through careful menu layout, where the input, output and
more explanation are logically divided on the same interface page. The choice to move
back and forth is also provided through the “Previous” and “Next” buttons. Java
program snippets for the main software modules embedded in the prototype are
presented in Appendix E. In particular, the Java methods/classes that support the
simulation and explanation tasks are provided.
5.7 Simulation Results and Discussion
In this section, the simulated results are presented as a collection of computer
screenshots. The correctness of the outputs is verified by the students and chemistry
instructors. At the end of a simulation, the simulator returns the final products formed,
as well as the following simulation results and explanations:
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• The entire reaction route of a qualitative simulation. This output result helps
explain why certain atom leaves (or approaches) a given organic compound. Such
result permits learners to study how a substrate’s molecular pattern is changed from
one reaction to another (Figure 5.20).
• The qualitative model representing the chemical process specification used in
predicting the behaviour of a chemical reaction (Figure 5.21).
• A causal graph that depicts the reacting species used, the intermediates produced,
and the cause-effect chain of chemical parameters in the simulation (Figure 5.22).
• The whole set of the parameter state histories (the parametric values) assigned to
each quantity in the reaction simulation. This is called a piece of “history” (Figure
5.23).
• The atom property table that contains the chemical states possessed by each reacting
unit during simulation (Figure 5.24).
• The whole set of view pairs used in the simulation (Figure 5.25).
As it is claimed, all the outputs are produced dynamically (on-the-fly). There is no
precoded reaction route in the program as in the traditional software development
approach of chemistry educational programs. Table 5.7 presents a summary of the
computer screenshots together with the objectives they serve, and the questionnaires
that test the achievement of the objectives. After analyzing all the computer outputs, a
mental shift in each student is expected as the consequence.
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Table 5.7: Computer screenshots, objectives and the questionnaires used to test it.
Computer screenshots Educational objectives Questionnaires that test the
achievement of the objectives
Reaction route of a qualitative simulation (Figure 5.20)
• Promote conceptual understanding
• Promote ability to articulate various aspects of a reaction
• “Effectiveness of the explanation of QRiOM” survey form
• From interviews
QPT model for organic processes (Figure 5.21)
• Promote ability to articulate various aspects of a reaction
• “Usefulness and Helpfulness of QRiOM” survey form
• “Effectiveness of the explanation of QRiOM” survey form
• From interviews Causal graph (Figure 5.22)
• Promote conceptual
understanding • Promote ability to
articulate various aspects of a reaction
• “Effectiveness of the
explanation of QRiOM” survey form
• “Usefulness and Helpfulness of QRiOM” survey form
• From interviews
Parameter state histories (Figure 5.23)
• Promote conceptual understanding
• “Usefulness and Helpfulness of QRiOM” survey form
Atom property table (Figure 5.24)
• Promote conceptual understanding
• “Effectiveness of the explanation of QRiOM” survey form
View pair used in each reaction step (Figure 5.25)
• Promote conceptual
understanding
• “Usefulness and Helpfulness of
QRiOM” survey form
5.7.1 Reaction Route
In QRiOM, a substrate’s structural change is represented in 2D format, resulting in the
so-called “reaction route” of a simulation. Two examples of reaction routes generated
by QRiOM are depicted in Figure 5.20. When organic reactions are described in this
way, the product of an organic reaction can be readily predicted, without recourse to
memorization. The reaction route gives the step-by-step change of the molecular
structure of an organic substrate. The tool can explain not only the steps it takes during
the reasoning process, but also the reasons for following these steps. This kind of
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explanation requires an explicit representation of the domain knowledge, described in
qualitative terms. Inspecting such a 2D representation can promote the conceptual
understanding of students.
( a) Step-by-step change of the molecular structure of an organic substrate (SN1 example)
(b) Step-by-step change of the molecular structure of an organic substrate (SN2 example)
Figure 5.20 Screenshots showing two reaction routes generated by QRiOM at the end of a simulation.
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5.7.2 QPT Model
Students typically have problems in describing the chemical parameters needed to solve
the problem. This is due to lack of the necessary chemical intuition, especially on how
to relate the parameters within a situation. When inspecting a model, students have to
articulate relationships between entities and dependencies. This can help improve their
reasoning ability. A screenshot of model inspection page is shown in Figure 5.21.
Testing different substrates in laboratory can be costly and sometime unsafe. The
simulator can solve this problem, as the user can repeat any number of times a given
reaction. Furthermore, students have trouble in thinking about the reasons for justifying
the activation of an organic reaction. The tool helps in a way that each constructed
model for a simulation task is presented for student inspection. The main goal of letting
students inspect the qualitative model is that they can articulate ideas behind the design
of the various slots in a QPT model. For example, the “quantity-condition” can be used
to justify why a process would start/stop.
Figure 5.21 A computer generated QPT model.
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5.7.3 Causal Graph
Much of the explanation used by QRiOM is achieved by tracing the effect propagation
through ontological modelling constructs of QPT. For example, during each reaction
simulation, a causal graph (Figure 5.22) is generated that shows the use of the
qualitative proportionality statements in the QPT models. Inspecting parameter
dependency and their direction of change can help a learner to pick up the underlying
concepts much better than merely memorizing the reaction steps or formulas. Causal
models help learners to rationalize why a particular process occurred. This can lead to
a deeper understanding of chemical processes. The four domain experts (chemistry
lecturers at University of Tenaga Nasional) have commented that the representation of
causality in the model generated by QRiOM is acceptable and valid.
Figure 5.22 A causal graph generated by QRiOM that enables learners to examine the cause-effect relationships of chemical parameters during reasoning.
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5.7.4 Parameter State History and Atom Property Tables
Learners can also browse the behavioural change of parameters belonging to each
reacting units (Figure 5.23) in which all the units (nucleophiles and electrophiles) used
in an organic reaction simulation is populated to a pull-down list. When a species is
selected from the list, the whole occurrence of the selected atom can be viewed. As an
example, under the “Charge History” title, the “[neg] [nil] [nil] [neutral]” can be
interpreted as “the initial value of Bromide ion is negative, it is not involved in the first
and second reaction steps (hence “nil” is used), the ion is used in the last reaction step
and this step will turn its charge to neutral”.
Figure 5.23 The states of chemical parameter of each reacting species involved in a simulation task can be examined in greater detail.
The values assigned to the chemical parameters during simulation are recorded in
special-purpose data structures for future retrieval. One such structure is the atom
property table (Figure 5.24a). These results can then be used to generate the necessary
reaction route (Figure 5.24b). The structure of the final product can be easily drawn
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from Figure 5.24a. For example, when the charge on “C” is positive (A1, Figure
5.24a), then a positive sign is assigned next to the “C” atom (B1, Figure 5.24b).
Likewise, at A2 of Figure 5.24a (under “After step 3” heading), the “C’ regained its
stability and this change is reflected at B2 of Figure 5.24b.
A1
A2
(a)
B1
B2
(b)
Figure 5.24 (a) The chemical states possessed by each reacting unit during simulation are stored in the atom property table (b) A reaction route drawn from using the data values in the atom property table.
When the three outputs: (1) causal graph, (2) parameter state history, and (3) atom
property table are examined together, the students are expected to relate various aspects
in a reaction such that they are able to explain an organic reaction in a more elaborate
way. This will lead to an improvement in one’s overall conceptual understanding of the
subject.
5.7.5 List of Reacting Species (View Pairs)
Since the majority of chemistry students have difficulties identifying the right view
pairs for processes activation, the tool will also generate the whole set of view pairs
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used in the simulation (Figure 5.25) thus informing the learner of the type of functional
units that activate a given process. For instance, “H+” and “CH3CH3CH3COH” are
reacting species (an electrophile and a nucleophile respectively) that activate the “make-
bond” process. The process also generates an intermediate called
“CH3CH3CH3CO+H2” (see “After Step 1” heading). Recall that this is the
intermediate product after the “First reaction step” as given in equation 3.1. This output
is seen as useful to the students (survey results are presented in Chapter 6).
Figure 5.25 The choice of reacting units for each reaction step and the intermediates produced are displayed for further inspection.
5.8 Conclusion
This chapter fulfilled three objectives. First, a qualitative reasoning framework for
organic reaction simulation and explanation has been developed. The chapter described
the development of the qualitative reasoning famework for the simulation of organic
reactions. Particularly, the chapter presented the algorithms that enable model
automation, chemical process reasoning, and the generation of causal graphs. The
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design of the internal structures for storing the atoms and molecules together with their
associated methods for implementation are also discussed. The reasoning framework
can be extended to support other organic mechanisms. The extensibility only relies on
additional facts to be placed in the chemical knowledge base. With such a design, many
learning software can be developed with minimal modification to suit their very unique
needs or features. Second, the different types and roles of chemical knowledge at
different abstraction levels have been defined. Third, a small set of chemistry ontology
called OntoRM for use during simulation has been developed. The application of the
OntoRM ontology helps the reasoning engine makes correct prediction. This feature is
not found in the QALSIC software. In particular, this chapter answered two research
questions: (1) “How can the domain knowledge (represented in QPT) and the OntoRM
ontology be effectively used?” (2) “How can knowledge validation be carried out?”
The first research question was answered by presenting the two-tier architecture for
knowledge base with their clear division of functions and roles. This is not found (or
rather unclear) in QALSIC. The second research question was answered with a number
of validation examples. This chapter also provides the protocol when interacting with
QRiOM simulator prototype, where “How” the system is used is described. The
development of QRiOM is based on the qualitative reasoning framework. The
simulation results presented in this chapter have been verified by the chemists, they
commented that the results of simulation matched those written in textbooks. The
various forms of outputs serve as the “explanation” to a chemical reaction or
phenomemon being learned. We shall see in the following chapter that QRiOM can
assist chemistry students learn organic reactions through the “explanation” pedagogy
embedded in the software.
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Chapter 6 Evaluation of QRiOM
6.1 Introduction
The main objective of QRiOM is to help learners gain a better understanding of the
fundamentals of organic reaction concepts and to improve their reasoning ability by
analyzing the multiple forms of output generated by the software. To test the
achievement of this objective, a preliminary evaluation of QRiOM was conducted upon
its completion. This chapter presents the feedback of students after using the software
tool. The survey comprised questionnaires, interviews and QRiOM hands-on. The
survey has three objectives. First, it is to collect the students’ receptivity towards using
the tool. Second, the survey was to find out the effectiveness of the explanation
generated by the QR/QPT approach. Third, we would like to know whether the
interface design can satisfy students from chemistry background (“user friendliness” is
the focus). We can collect some feedback from the students even though this study was
not intended to be highly prescriptive. Some evidence that QRiOM can improve
learning is also provided.
6.2 The Evaluation Context
QRiOM is not a courseware (neither it is an ITS), but a qualitative simulator that has
embedded intelligence to explain its reasoning by tracing the functional dependency of
parameters governed by the modelling constructs of the QPT. We focus on two main
criteria when designing the questionnaires, namely, effectiveness and user-friendliness.
The former criterion is to collect general feedback of the respondents about how
effective the tool is in nurturing their conceptual understanding towards the subject
while the latter criterion aims at collecting general view of the students if it is a user-
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friendly tool. The evaluation was conducted based on qualitative and quantitative
approaches. Murray (1993) stated that qualitative approaches provided information as a
function of personal interaction and perception. The most commonly used technique for
data gathering when conducting qualitative research is subject-based that includes
questionnaires, observations, interviews, and focus groups (combining elements of both
interviewing and observation). Two types of subject-based evaluation techniques were
used in this work, namely questionnaires and interviews. We also conducted the
quantitative approach. Quantitative evaluation is mainly about identifying the
characteristics of a situation or setting (Shute and Regina, 1993). Questionnaires are the
most commonly used technique for data gathering when conducting quantitative
research. For example, we computed the total counts of responses for each question in
a survey form (e.g. the total number of responses for “I find qualitative reasoning
easy”). Other techniques used in evaluating educational materials include pre-test and
post-test, which is also used in this study.
Background of participants: The respondents include chemistry lecturers, IT lecturers
and undergraduate level chemistry students (from different academic standings). They
are invited to serve as evaluators upon completion of QRiOM’s development. Only a
small group of chemistry students enrolled in an introductory chemistry class was
recruited since QRiOM currently has the status of a prototype. We would like to find
out if they can learn and understand better when exposed to the tool and if they would
like to see other features in the software. The IT lecturers were interviewed to provide
general comments on the system’s GUI design, technical contents, overall functionality
and user-friendliness of the software. The respondent categories are summarized as
follows:
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1. Twenty chemistry students
• To collect feedback on the effectiveness of the explanation returned by QRiOM
• To collect feedback on the usefulness and helpfulness of QRiOM
• To provide comments on the GUI design
2. Supervisors (for overall objectives fulfilment)
• Two chemists and one AI expert
• To verify the prediction results produced by the tool
3. University colleagues
• Four other chemists
• To collect general views and comments for enhancement
• To see if there is anything seriously lacking in the software
• Three information technologists
• To provide comments on the GUI design
This chapter only discusses the evaluation results from the chemistry students, as the
tool is intended to assist them in their learning.
6.3 Procedures Used for Conducting the Questionnaires
The evaluation includes a lecture on the QPT ontology and a tutorial on QRiOM,
particularly focusing on some common ontological modelling constructs and the notion
of qualitative causal graphs. After introducing the modelling language and a
walkthrough on QRiOM, time limited hands-on sessions began. At the end of each
session, students are given a survey form. Table 6.1 summarizes all the questionnaires
and the associated educational objectives that each achieved. Figure 6.1 shows the
procedures used in conducting the system evaluation. Section 6.3.1 – Section 6.3.6
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discuss the procedures of the questionnaires, the survey results with discussion as well
as the achievement of specific educational objective(s).
Table 6.1: Questionnaires and the fulfilment of respective educational objective.
Survey forms Educational objectives to
achieve
Survey
Results
Snapshot and/or component
in the framework that
support the fulfilment of the
specific objective
1. “Pre-Questionnaire” survey form
• Improve in conceptual understanding
• There is a positive mental change (high scores were given in post-test evaluation)
Figure 6.6
• The framework component that is responsible for the achievement of this objective is the reasoning engine
2. “Post-Questionnaire” survey form
3. “Effectiveness of
the explanation of QRiOM” survey form
• Improve in conceptual understanding (especially in behavioural and causal aspects of a reaction)
• Able to articulate (can provide longer answers)
Figure 6.8 • Reaction route (Figure 5.20)
• QPT model inspection page (Figure 5.21)
• Causal graph (Figure 5.22) • Atom property table for the
substrate (Figure 5.24) • Framework component that
is responsible for the achievement of this objective is the causal explanation generator
4. “Usefulness and Helpfulness of QRiOM” survey form
• There is a positive mental change (more confidence in attempting new problems)
• Able to articulate
Figure 6.10 • Reaction route (Figure 5.20)
• Causal graph (Figure 5.22) • Parameter history during
simulation (Figure 5.23) • View pairs choice list
(Figure 5.25) • The entire simulation
engine is responsible for this objective
5. “Student understanding towards QPT ontology” survey form
• Students can understand the ontology
• Students can appreciate the ontology as a new knowledge capture tool
Figure 6.4
Not applicable
6. “Opinion about applying qualitative reasoning and modelling in chemistry” survey form
• Students find the technique appropriate for chemistry reaction simulation
• Students will explore this new technique of performing chemistry reaction simulation
Not applicable
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START
END
Survey forms are given to collect information about students’ knowledge
in core areas of organic reactions (This is Pre-Questionnaire)
QPT briefing is delivered; in order to understand some terms used in the tool
Opinions about QPT and qualitative reasoning approach are sought
QRiOM problem solving model is explained to the students, i.e. how to
interact with the software tool
Students are given 20 minutes hands-on using the tool
Survey forms are distributed to collect students’ opinions about the
effectiveness of the explanation facility of QRiOM
Perspectives on user friendliness of the tool are collected (via an interview)
Survey forms are distributed to measure the usefulness and helpfulness of
QRiOM
Survey forms for Pre-Questionnaire are given again to see if a student’s
conceptual understanding in the core areas of organic reactions has improved
(This is Post-Questionnaire)
Figure 6.1 Flowchart of the QRiOM evaluation exercise.
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6.3.1 Students’ Feedback on the use of QPT and Qualitative Reasoning
Approaches
Participants were given a brief introduction to QPT and qualitative reasoning approach
then they attempted the questions in Figure 6.2 (see also Figure C.1 in Appendix C) and
then they answered the questions in survey form as presented in Figure 6.3 (see also
Figure C.2 in Appendix C). Thirty minutes are allocated for this session.
The objective of this survey is to collect opinions and views from the students on the
use of QPT-based reasoning. This survey enables us to collect feedback about the
suitability of QPT-based reasoning as the new means for performing simulation and
prediction in the application domain. Examples of the survey questions are given in
Figure 6.2 and Figure 6.3.
Strongly
Disagree
Disagree Neither
Agree Nor Disagree
Agree
Strongly Agree
Q1 The identification of quantities (parameters) helped me to establish the functional dependency among them.
1 2 3 4 5
Q2 The specification represented using QPT makes it easy to understand the organic processes (reaction steps) that are involved in a chemical reaction simulation.
1 2 3 4 5
Q3 The flow of the reasoning is more systematic when a specification that captures the chemical knowledge and intuition is there. (like the one given in the attachment – a QPT model)
1 2 3 4 5
Q4 I still don’t know how to read the diagram in the attachment even though it is already taught.
1 2 3 4 5
Q5 The QPT specification describes almost exactly what I have in mind.
1 2 3 4 5
Q6 There are still many concepts implicit in the chemical reaction but I don’t seem to see them in the model.
1 2 3 4 5
Figure 6.2 Examples of survey questions used for measuring students’ understanding towards QPT.
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Strongly Disagree
Disagree
Neither Agree Nor Disagree
Agree
Strongly Agree
Q1 The likelihood that I would read further about qualitative reasoning & modeling is high.
1 2 3 4 5
Q2 I don’t like the trouble of going through modeling and simulation before real experiment.
1 2 3 4 5
Q3 If I want to explain reaction mechanisms to my friends, this is the type of formal way that I’m looking for.
1 2 3 4 5
Figure 6.3 Sample questions in a survey form that collect students’ opinions about qualitative reasoning and modelling approaches.
Survey results and discussion: Based on a short lecture (30 minutes) about qualitative
reasoning and modelling using the QPT technique for learning organic reactions,
respondents were asked to indicate on the scale given in the survey form. The
following implementation levels were considered: 5 = strongly agree, 4 = agree, 3 =
neither agree nor disagree, 2 = disagree, 1 = strongly disagree. Based on the survey
questions in Figure 6.2, the average score for Q1 – Q3 is 4 (“agree”). The score
indicates that the students find the modelling constructs of QPT helpful. In particular,
the use of the constructs in a QPT model helps promote a student’s understanding in the
basic behaviour of organic processes. On the other hand, the average score for Q4 – Q6
is 3 (“neither agree nor disagree”). This result somewhat reflects that majority of the
students were still blurred with the QPT’s various slots (i.e. not sure how to match their
mental states to the modelling constructs of QPT) since this is the first time the QPT
technique was introduced to them. Based on the survey questions in Figure 6.3, the
average score for Q1 is 3 (this is a neutral decision, indicating that they find the AI
approach something new and not sure whether they would explore it further); the
average score for Q2 is 2 (“disagree”, this means that they do not mind trying out
modelling before real experiment), and the average score for Q3 is 4 (“agree”, the result
indicates that the approach is somewhat promising).
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Figure 6.4 Students’ responses towards understanding QPT and qualitative reasoning
approaches.
When the technology behind the reasoning engine of QRiOM is introduced (i.e. the
concept of qualitative reasoning based on QPT), 13 out of 20 students commented that
the technique is rather difficult to understand. Figure 6.4 somewhat reveals that the
group of chemistry students felt that the reasoning technique is difficult to understand,
but they said the results returned by the software are useful. This is why when
designing the software we hide all the complexities behind the GUIs (i.e. not to let the
students “see” QPT reasoning at the forefront of the learning tool).
6.3.2 Assessment of Students’ Skills in Core Areas of Organic Reactions – The
Pre-Questionnaire
The survey continued by getting the participants answered Pre-Questionnaire. Ten
minutes are allocated for this session.
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This questionnaire is to assess student skill and knowledge in core areas of organic
reactions before using the tool. Sample questions are presented in Figure 6.5. These set
of questionnaires were distributed twice to observe the pre- and post- differences once
was before the students were exposed to the tool and once after they had the hands-on
session.
Skill-Set Area Poor Fair Good Expert
1. Fundamental principle of organic reactions
2. SN1 and SN2 mechanisms
3. “Make-bond” and “break-bond” processes
4. Parameters dependency in an organic reaction
5. Use of reacting species in “make-bond” and “break-bond” organic processes
6. Classifying structural units as nucleophiles or electrophiles
7. Chemical theories that support an organic reactions
8. Rule-of-thumb use in predicting final product(s)
Figure 6.5 The survey form for course competency assessment distributed before/after using the simulator.
6.3.3 Assessment of Students’ Skills in Core Areas of Organic Reactions – The
Post-Questionnaire
Participants were then briefed with the problem solving model of QRiOM. After that,
they were exposed to the tool and then they were asked to rate their competencies for
several technical skills stated in Post-Questionnaire. Twenty minutes are allocated for
this session.
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This survey aims at collecting the opinions from the students to observe if there is a
mental change/shift experienced after using the tool. It is also to find out if the students
can do better in solving new problems. The survey questions used for Pre-
Questionnaire (Figure 6.5) are used again.
Survey results and discussion: The chemistry students are observed to learn better in
terms of their conceptual understanding of the reactions. Based on the feedback, it can
be concluded that they could do better in solving new problems as a result of acquiring
skills in knowledge articulation (Figure 6.6).
Figure 6.6 Student pre-test and post-test responses to the core skills.
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6.3.4 Assessment of Effectiveness of QRiOM’s Explanation Facility
After answering the “before-and-after” sets of questionnaire, the students were told to
continue with the questions that aimed at assessing the effectiveness of the explanation
facility of QRiOM. Ten minutes are allocated for this session.
This questionnaire is to solicit the students’ responses towards the explanation
generation capability of QRiOM. This survey enables us to assess if chemistry students
are able to articulate knowledge after analyzing the various ways of presenting the
results of a simulation. Figure 6.7 gives the survey questions used in this questionnaire.
Knowledge aspects Not at all
To a limited extent
To a moderate
extent
To a great extent
1. The conditions to start/stop a chemical process
2. The proper identification of nucleophile and electrophile to activate a chemical process
3. Cause-effect propagation among chemical parameters
4. Behavioural change of a substrate (in terms of its charge, lone pair changes)
5. The production of an intermediate: the why and how?
6. Fundamental concepts of SN1 and SN2
7. Fundamental concepts of “make-bond” and “break-bond” processes
Figure 6.7 Questions in the survey form for the measure of explanation-based learning in skills reinforcement.
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Survey results and discussion: Figure 6.8 indicates the overall results for each rating
score presented in Figure 6.7. Particularly, 80% (16 out of 20) of the respondents felt
that their knowledge on the two aspects as described in question 3 and question 4 has
been improved to a great extent. Namely, students seemed to find analyzing the
reaction route the cause-effect helpful in learning how an organic process takes place
and the overall changes undergone by the organic substrate. They have never thought
of using a causal graph or even the reaction route to express the overall behavioural
change of substrates. This supports the fact that the tool has potential in helping the
students understand organic chemical reactions.
Figure 6.8 Students’ feedbacks on the extent to which the tool improves one’s knowledge in terms of skill reinforcement through explanation-based learning.
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6.3.5 Assessment of the Usefulness and Helpfulness of QRiOM
After answering the questionnaire for measuring “effectiveness of explanation facility”,
the respondents were told to continue with the questions that aimed at assessing the
usefulness and helpfulness of the tool. Ten minutes are allocated for this session.
The objective of this questionnaire is to determine if the tool is useful (e.g. students are
more confident in answering new questions) and helpful (e.g. the materials presented
motivate the student to learn). Sample questions are given in Figure 6.9.
Please respond to the following statements about your learning experience:
Strongly agree
Agree Neither agree nor disagree
Disagree
1. I gained more confidence after using the simulator prototype (QRiOM) – for usefulness test
2. The reaction route and the use of pairs of nucleophile and electrophile helped me to understand better the essential reacting species used in the entire reaction – for usefulness test
3. The chemical properties that modelled as qualitative proportionality helped me to understand basic concepts of a reaction – for usefulness test
4. General chemical behaviour and chemical knowledge represented in the QPT process model allowed me to acquire essential background knowledge before going to see the output and explanation – for helpfulness test (motivated?)
5. The cause-effect demonstration in tabulated form encouraged me to think more critically towards the problem task at hand – for helpfulness test
6. My comment to this statement:
“kalau saya dengar, saya lupa (If I hear, I forget)
(≅ merely attending lectures) kalau saya lihat, saya ingat (If I see, I remember)
(≅ just doing experiments in lab) kalau saya buat, saya tahu (If I do, I understand)
(≅ simulation hands-on using QRiOM)” – for helpfulness test
Figure 6.9 Examples of the survey questions for the measure of usefulness and helpfulness of QRiOM in a student’s learning endeavour.
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Survey results and discussion: Figure 6.10 reveals that majority of the students agreed
that the tool is useful (in terms of the confidence gained) in their learning process. On
the other hand, helpfulness covers the value of the materials presented as well as the
ease with which a user can operate the application. Students gave very high score to the
tool on this aspect. They found the tool helpful because it motivates the student to
learn, especially in several areas such as:
• They are allowed to choose different combinations of <substrate, reagent> pair.
• They can repeatedly run the same reaction.
• The tool offers certain degree of interactivity.
• The tool provides adequate coaching.
In addition, when interviewed, slightly less than half of the students representing 40%
(8 out of 20), felt that they underwent a change of reasoning, as the explanation
provided by the software does reveal the chemical intuition needed to solve the organic
reaction problems.
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Figure 6.10 Students’ feedbacks on helpfulness (motivated) and usefulness (gain more confidence) of QRiOM.
6.3.6 Comments on Graphical User Interface Design
An interview was conducted after the students have been exposed to the tool. They
were asked to give comments on some Graphic User Interface (GUI) design aspects,
such as clarity of interface (80%), interface consistency (70%), and meaning of
commands (60%). Percentage in bracket indicates the satisfaction level. Attitude
towards using the software have also been measured, including several affective
components, for example, “I like the tool” or “I dislike the tool”. The results showed
that students with positive attitude outperformed those with negative attitude (14 out of
20). Most of them were very pleased to have had the chance to use the tool (16 out of
20) and majority of the respondents felt that there was too much emphasis on the QPT
terms (mentioned as difficult). They suggested more lectures should be given to them
(17 out of 20). The responses collected seem very encouraging given that this is the
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first exposure of the chemistry students to using/evaluating the computer-based learning
tool. We will take into the consideration the comments made by the students to
improve on the software tool. One of the efforts is to convert most of the QPT terms
into student-friendly terms, so that this does not become a barrier for them.
6.4 Conclusion
The initial evaluation of the QRiOM and its explanation facility has been carried out.
The evaluation result supports the hypothesis that qualitative simulator tools can be
valuable aids for improving conceptual understanding of the basic principles of organic
chemistry processes. In general, chemistry problems presented in textbooks could be
difficult to understand by students because the diagrams and figures are in static form.
The educational benefits offered by QRiOM include the ability to take learners into
environments otherwise inaccessible by conventional face-to-face teaching and the
ability to create a dynamic and interactive environment for learning. A set of logically
related research questions has also been answered, such as:
• Will the tool help improve a student’s conceptual understanding of the subject?
The answer is also “yes”. Results are shown in Figure 6.6.
• Is the explanation generated by QRiOM effective in enhancing students’
understanding of the subject? From the feedback, it is effective. Results are shown
in Figure 6.8.
• How useful is the software in terms of helping the students to promote their
understanding and articulation of chemistry concepts in relation to an organic
reaction? The answer is positive as mostly said the software is useful and helpful.
Results are shown in Figure 6.10.
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• Will the chemistry students undergo mental change so that they are able to explain
chemical phenomenon in a more elaborate way? The answer is there was a change
in one’s mental design when interacting with the software, in which a given
organic reaction can in fact, be described better by the chemistry students.
• QALSIC was never evaluated with actual student responses, as such the assessment
of QRiOM made a contribution to the literature of QR-related system.
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Chapter 7 Conclusion
7.1 Thesis Summary
There are three main objectives of this thesis. The first and foremost task is to develop
a conceptual framework of qualitative reasoning that is powerful enough to perform
organic reaction simulation and to reproduce the behaviour of selected organic
mechanisms. This task has been accomplished. The second objective is to apply the
framework to the task of providing qualitative explanations for observed chemical
phenomena using the QPT formalism. This too has been accomplished. Third,
implementation of a qualitative simulator to perform behaviour prediction and
explanation that is of help to chemistry students. From the results of the evaluation, the
simulator has achieved its objectives.
Overall, this thesis presents the work on the design of a qualitative reasoning framework
for the simulation of SN1 and SN2 mechanisms in organic reactions. The development
of a simulator prototype, QRiOM, aimed to simulate organic chemical reactions for
learning purposes has also been described. QRiOM can predict and explain the
formation of the final products, given an initial situation comprising an organic
substrate and a reagent. In particular, a principled approach for automating model
construction has been proposed. The OntoRM ontology has also been developed to
validate the use of chemical knowledge during simulation. The ontology can be
extended to cater for more descriptions of reaction mechanisms.
The fundamental assumption behind this research is that the modelling and simulation
techniques based on QPT ontology and qualitative reasoning technique provides an
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effective approach that is capable of explaining phenomena in organic reactions in a
natural way (much like the way a human expert would explain it). It is confirmed that
the explanation generated by the software tool can help improve a student’s
understanding of organic reaction and mechanism.
The simulator is similar in idea with some existing systems based on qualitative
reasoning. However, the software is supported by a two-tier knowledge base, namely
the OntoRM reaction mechanism ontology (purely used as a validation tool) and a
chemical knowledge base that stores the essential domain knowledge (the partial
knowledge needed by the QR approach), rather than just one layer as reported in
reviewed literature. All of the functional components presented in Chapter 5 have been
implemented in software using Java and Prolog.
Conventional teaching and learning of the subject is facing several limitations. There
are also some problems faced by other approaches of program development. The
limitations and problems are summarized below:
1. Most students when interviewed said that they learn the subject by memorizing the
reaction steps and the entire chemical equations.
2. In classrooms, students are taught how to use arrows to move electrons around in
order to predict the outcome of a reaction. Overall change made to a reaction is
difficult to be visualized at once (on the white board).
3. In the conventional approach, reaction prediction is performed by finding a route
through searching the entire state space (the precoded routes in the knowledge
base). Consequently, the software cannot handle new problems that are not coded
in program and large amount of storage is needed since all possible reaction routes
need to be stored in the KB for searching and retrieval.
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4. Traditional chemistry educational software is inadequate to promote understanding
or explain toward its results because the traditional method does not link
“reasoning” to problems.
The approach used in this work is able to overcome the limitations of conventional
teaching methods. This work addressed all four of the aforesaid problems by providing
techniques, algorithms, prototype, and the evaluation results of the prototype.
7.2 Results and Contributions
No previous work has been reported on solving organic reaction problems using
qualitative reasoning approach. In this work, qualitative simulation based on QPT
models is used as a means to provide explanation to chemistry students. Prior to this
work, the domain has never been tested with QPT-based reasoning. This thesis starts
with a critical review of the QPT ontology and then used it to represent chemical
knowledge qualitatively in order to model the behaviour of organic reactions and
mechanisms. Qualitative reasoning algorithms for the simulation of numerous organic
chemical reactions involving different organic substrates were then designed. All the
components in the reasoning framework have been implemented in QRiOM. QRiOM is
viewed as useful and effective by chemistry learners, consistent with the fact that
students’ conceptual understanding is improved. QRiOM is also the first chemistry
education software that can generate multiple forms of textual explanation (in order to
justify a simulated result). The explanation follows almost isomorphically from the
QPT model reasoning.
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The contributions of this thesis concern different areas of research related to qualitative
modelling, model automation, qualitative simulation and prediction, interactive
explanation and chemistry educational software, each of which is discussed in detail in
the following subsections (Section 7.2.1 – Section 7.2.3).
7.2.1 Conceptual Framework Development
A new technique for modelling the behaviour of organic reactions has been explored.
The new technique referred here is qualitative modelling of domain knowledge using
QPT ontology. The approach used to predict the outcome of “A + B” (A reacts to B) is
by performing qualitative reasoning based on the QPT models constructed for the two
generic processes. In this work, the formation of the final product is explained by the
“mechanism” that is used to accomplish the prediction task. The suggestion of a
suitable chemical process is determined by recognizing the nucleophilic and
electrophilic centres of the < substrate, reagent> input pair while a predicted outcome is
explained by means of the specific mechanism suggested by the reasoning engine. An
organic mechanism used for the reaction will consist of the series of organic processes
used in the conversion of the reactants to the final product by emphasizing on the state
change of chemical parameters that had occurred.
The reasoning framework embodies a number of functional components for a wide
range of organic processes and mechanisms simulation. The components in the
framework include (1) Substrate Recognizer (for checking user selection, and returns
the “type” of the input as either a nucleophile or an electrophile), (2) Model Constructor
(for automating the construction of QPT models based on the identity of user input), (3)
Reasoning Engine (for actual simulation), (4) Causal Model Generator (for constructing
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causal graphs to produce accounts of behaviour), (5) Explanation Generator (for
generating explanation to justify a simulated result), (6) Molecule Update Routine (for
keeping track of the structural change of the substrate, from one organic reaction to
another), (7) Knowledge Validation Routine (for ensuring correct piece of chemical
data is used), (8) OntoRM ontology (for defining chemical knowledge related to
reaction mechanism), and (9) Chemical Knowledge Base (for storing information such
as chemical facts and theories).
7.2.1.1 QPT as the Knowledge Capture Tool
QPT is chosen because the modelling ingredient of this particular QR ontology provides
good grounds for describing processes in conceptual terms with notions of causality
which can be used for explaining the behaviour of chemical systems. The ontology also
allows representation of chemical process elements at the finest level of granularity.
Problem characteristics and the behaviour of organic reactions and mechanisms were
sought and studied. Dialogues with chemists were conducted to find the possibility of
representing the required knowledge in qualitative terms using QPT. Collecting
intuitive and causal aspects of chemists’ mental models helped us in designing the
cognitive steps used in the reasoning algorithms. These types of knowledge enabled us
to establish functional dependency of chemical parameters during a reaction using the
modelling constructs of QPT (which also support cause-effect propagation via its
direct/indirect influences).
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7.2.1.2 Model Automation
We classified chemical processes for a variety of substrates into a few organic
processes. Chemical processes needed in the simulation were identified as “make-bond”
and “break-bond”. This simple classification scheme of two processes enables the
assembly of general chemical behaviour and chemical theories needed to support model
automation. Multiple chemical equations under SN1 and SN2 mechanisms were studied
in order to collect their general behaviour. A set of rules has been formulated which
specify how the chemical theories of organic processes can be represented using the
modelling constructs of QPT. When the logical steps were obtained, the algorithm that
enables model automation was developed. The generalization of such behaviour helps
promote model reusability. The model automation algorithm provided in this thesis
helps solve partly the knowledge acquisition bottleneck.
In the course of developing the conceptual framework, we produced the following
results:
1. Reusable chemical processes (hence the models) were identified. The reusable
processes are “make-bond” and “break-bond”.
2. Model automation logics were formulated. Automating the construction of QPT
models is made possible by first identifying the type of the reacting species, then the
chemical process that can take place. Last, the common set of chemical theories
represented in QPT is retrieved for composing the process model.
3. The mental attributes of human chemists when solving organic reaction problem
were represented using the modelling constructs of QPT.
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7.2.2 QRiOM – A Tool for Explaining Organic Reactions
The simulator prototype, QRiOM integrates all of the components stated in Section
7.2.1, and it produces acceptable behaviour prediction for all the reactions as stated in
Chapter 1 (course scope section). The simulation results and the textual and
diagrammatic explanation produced by QRiOM are not precoded but they are generated
via causal model tracing and interpretation. The simulation algorithms generate
explanation following the same structure from a QPT-based reasoning and as such no
complicated explanation technique is required. The work produced better explanation
as compared to LHASA (a collection of equations are used and very complex molecular
structures are presented) and QALSIC (no explanation provided), in terms of more
natural and less technical in its presentation. The prototype is able to handle new cases
since only general chemical principles of organic reactions are stored and not the
specific reaction routes that produce the final product. Furthermore, since reaction
routes are not precoded, the entire program takes up very little space.
Traditional chemistry learning software generates results without proper explanation.
QRiOM returns the following textual and diagrammatic outputs and explanations at the
end of a simulation task: (1) The final products of a reaction, (2) Suggested organic
mechanism used to predict the product, (3) The qualitative models used in predicting
the behaviour of a chemical reaction, (4) A causal graph that depicts the reacting
species used, the intermediates produced, and the cause-effect chain of chemical
parameters in the simulation, (5) The set of the parameter values assigned to each
chemical quantity in the reaction simulation, (6) The entire reaction route of a
qualitative simulation, and (7) The updated list of reacting species (electrophiles,
nucleophiles and new intermediates) before and after each chemical process. Most of
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these outputs are generated by two special purpose modules in the QRiOM simulator,
namely the Quantity Space Analyzer (QSA) and Molecule Update Routine (MUR).
Main results obtained from the QRiOM simulator prototype are:
1. The tool is able to make correct prediction for a large set of reactants, with no
precoded answers in the knowledge base. We have tested that new cases can be
handled such as adding new reactants by not making any change to the chemical
KB. The main reason for this achievement is that only chemical principles and
chemical theories are stored in the KB, not the precoded reaction routes.
2. Interactive explanation is achieved by tracing and interpreting causal models that
are created during qualitative simulation.
7.2.3 Evaluation Results of QRiOM
QRiOM does help in nurturing the conceptual understanding of a learner especially in
the understanding of the behavioural and causal aspects of organic reactions. An
evaluation study has been carried out with twenty first-year undergraduate students.
The results of the evaluation suggest that QRiOM is effective in terms of its ability to
promote understanding in learning organic processes through the inspection of the
explanation generated by the tool. The setup included paper-based pre- and post-
assessments concerning their skills in a few core areas of the subject. A questionnaire
was used to gauge the participants’ responses about QRiOM in terms of the helpfulness
and usefulness of the explanation generated by the software. Results from interviews
showed that, after using the software students are able to explain a chemical
phenomenon in a more elaborated way (i.e. providing a longer answer as they are now
more confident in solving the problem). Mental change of chemistry students who
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participated in the QRiOM evaluation was surveyed. Use of the tool can help oneself to
discover his/her mental change, such as realizing or knowing his/her own reasoning
ability. The achievement of these learning objectives is due to the “explanation”
pedagogy that is embedded in QRiOM that assists chemistry students learn organic
reactions through the study of functional dependencies of parameters and the causality
chain. As far as the application of the QPT-based reasoning is concerned, we achieved
positive results that met the educational objectives stated in Chapter 1. Major
accomplishments of applying the QR approach in solving the reaction simulation
problem for learning purposes are summarized in Figure 7.1.
Main results obtained from the evaluation of QRiOM are:
1. The tool has been evaluated in terms of its usefulness, helpfulness and effectiveness
in explaining chemical phenomena related to organic reactions. Overall, the results
are promising, i.e. the tool generally enhanced student knowledge.
2. QRiOM is viewed as useful and helpful where most of the student underwent
mental change when exposed to the software.
3. The tool helps nurture conceptual understanding of the learners especially on the
knowledge about the behavioural and causal aspects of organic processes.
4. The majority of the respondents agreed that the tool gave a good background on
solving organic reaction problems.
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Learning organic
reaction using
qualitative
reasoning
approach
Enhance
reasoning ability
Improve
understanding
Promote one’s
learning via the
“explanation”
pedagogy
Overcome traditional
program development
problem
Explanation follows
isomorphically from
the underlying QPT
reasoning
Cause-effect
relationships can
be examined
Figure 7.1 Accomplishment of the QR approach when implemented in a tool for learning organic reactions.
Apart from the abovementioned achievements, we have also made the following
contributions:
• OntoRM – a scheme for organizing and structuring chemical knowledge for reaction
mechanisms has been developed. The effective use of the chemical knowledge base
is achieved by applying OntoRM during modelling and simulation stages.
• Knowledge validation was carried out to avoid wrong simulation steps in a reaction
through the use of OntoRM. As a result, more accurate and reliable predictions can
be obtained.
• An essential part of the work, a scheme for hierarchical structuring of processes to
facilitate the effective use of knowledge was also developed. This is not found (or
rather unclear) in QALSIC.
• An analysis of application of the QR approach in inorganic versus organic reaction
simulation was carried out. The main finding is that organic chemistry reactions are
217
relatively easier to be modelled using QR approaches as compared to inorganic
chemistry reactions.
• The QALSIC program was investigated and tested with numerous inorganic
reactions. We have detected the main reason why the software returns incorrect
predictions for a large number of inorganic reactions. The software has been tested
with a mixture of inorganic reaction experiments and the conclusion is that those
invalid outputs are due to the nature of the problem domain rather than the
limitation in the underlying QPT reasoning. Simply said, the inorganic chemical
reactions are difficult to generalize compared to organic chemical reactions.
7.3 Limitations
Since this is the first attempt in testing the qualitative reasoning approach for solving
organic reaction problems and the tool is still in its infancy, there are several limitations
in this work. The limitations are summarized as follows:
• Our knowledge base contains only aliphatic compounds and a small set of reagents.
• 3D animation is not included.
• User modelling is not included.
• Users can only view the models but are not involved in building the model, i.e. the
entire modelling phase is without user participation.
7.4 Future Works
Several key challenges remain, such as expanding the chemistry process to include
more types of organic mechanisms and to further investigate the SMILES format for
representing each organic compound as a line notation in the knowledge base to achieve
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greater portability and reusability of the system component. The work will be
continued from a number of aspects. These include generating 3D animated multimedia
output (currently, outputs are in plain 2D format) and the development of a protocol
converter to handle protocol between the reasoning shell and the 3D output. A problem
ontology that handles user queries much like the one presented in Pah et al. (2007) is
also the direction of our future work. The main purpose of having the problem ontology
is to deal more specifically and accurately with questions that may be asked by the
learners. QRiOM can also be improved by adding pedagogical elements (such as the
different learning styles) in the “technogogical” three-dimensional (technology, content
and the pedagogy) learning environment as proposed by Idrus (2008). Extending
QRiOM to a full version learning software by embedding the user modelling module
and an assessment system is on the way. So far, the prototype does not support much
student-initiated exploration. A long term research direction has also been charted,
which is to build a graphical language to link between the qualitative simulator and a
graphic package. This is because one aspect of QR research that we see people have
not addressed is to build a graphical language between the reasoning engine (based on
some QR ontology) and graphic package itself (e.g. Model Science software). Many
simulators can only generate textual explanation but not graphical animation.
Bredeweg’s VisiGarp is a visual representation of the qualitative processes, which is not
what we discuss here. If the graphical language does exist, it will become the protocol
to communicate these two worlds: reasoning engine and graphic packages. This may
help to push more QR-based systems into the commercial world.
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7.5 Concluding Remarks
The qualitative reasoning approach based on qualitative process theory described in this
work has never been applied to the fields of organic chemistry and reaction
mechanisms. The reasoning framework (and the simulator prototype) is able to
generate similar outcomes as the one produced by chemists. Evaluation results showed
that embedding qualitative reasoning approach in educational software is useful to
nurture a student’s various learning skills. To conclude, this work combines qualitative
reasoning and ontologies in a problem-solving system, and generates explanations for
learners from the problem-solving system. After developing and testing the prototype,
we anticipate a fully usable system that can assist chemistry students not only in
understanding the subject, but also engaging them in building simple models as a means
to acquire knowledge. This research provides a good foundation for future works in the
application of qualitative reasoning approach in other subfields of the organic chemistry
course.
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Appendix A
A Summary of Systems Related to Qualitative Reasoning
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Table A.1: Examples of educational software employing QR approaches.
Application Area /
Name of System Researchers/
Authors
Brief Description
QSIM Benjamin Kuipers
• QSIM is the first qualitative simulation program; developed by Kuipers. The ontology used is constraint-based (Kuipers, 1986).
• The approach started with a set of constraints abstracted from a differential equation and proved that the QSIM algorithm is guaranteed to produce a qualitative behaviour corresponding to any solution to the original equation.
• His work also showed that any qualitative simulation algorithm will sometimes produce spurious qualitative behaviours: ones which do not correspond to any mechanism satisfying the given constraints.
• These observations suggest specific types of care that must be taken in designing applications of qualitative causal reasoning systems, and in constructing and validating a knowledge base of mechanism descriptions (Kuipers, 1993).
Intelligent Tutoring
Environment for Industrial
Training Operators (ITTs)
J.A. Vadillo & Díaz de Ilarraza
• The work by Vadillo & de Ilarraza (1995) describes an extension of the INTZA system, a tutoring environment for industrial training operators.
• The work concentrates on the potential use of qualitative models for generating explanation to help users or learners to learn a domain. Paradigms applied are qualitative simulation based on components (de Kleer and Brown, 1984) and Qualitative Process Theory (Forbus, 1984).
• In order to provide good behavioural explanation for simulations in INTZA, they extended domain models with a qualitative causal viewpoint. The causal model is obtained by applying causal ordering (Iwasaki and Simon, 1986) to the set of differential equations that describes the system.
• The work concluded that qualitative causal model is useful to generate explanation in ITTs.
MS-PRODS/
CPRODS for Learning
Complex Physical System
Intelligent Training System
Julie-Ann Sime
• Sime (1995) presents the rationale behind the design of a simulation based learning environment, the Model Switching PRO cessing Demonstration System (MS-PRODS).
• MS-PRODS, a learner-centred learning environment based on multiple qualitative models in order to promote better understanding of a process. Three qualitative and three quantitative simulations of the behaviour of the physical system have been implemented using the ITSIE tools.
• Emphasis is on the use of different domain models, both quantitative and qualitative to achieve understanding of a process.
• The work introduced seven dimensions to classify the different domain models and a mechanism to progress through the models, based on these dimensions. The system could use several strategies for model progression.
• In CPRODS (Sime, 1998), six qualitative and quantitative models were used. A trainee can solve problems or observe the expert demonstrate problem solving using multiple models, switching between them as and when necessary.
• One of its strength is that instructional design is provided. However, there is no reflection on the learning process.
• The design of the learning environment stresses on how the models are to be used to promote learning, in particular examining the model switching mechanism. This mechanism determines how to select and sequence the presentation of models to the learner during guided explorations of the domain.
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• Sime claims that an intelligent training system requires not just the development of tools and techniques for modelling and simulation but also some guidance on how to use qualitative models in learning environments.
• The work uses of Cognitive Flexibility Theory with assumptions that learning is greatly facilitated by guided, non-linear, multi-dimensional explorations of the content domain.
VModel
RoboTA
Kenneth D. Forbus et al.
• VModel software (Forbus, Carney, Harris & Sherin, 2001) was developed for middle school students, with an aim to prepare students to have skill in modelling.
• VModel uses a visual representation of modelling conventions similar to concept maps.
• Ureel & Carney (2003) presented a design of computational supports for students in visual modelling tasks. In this work, a visual representative language was developed for use with the middle-school students, because predicate calculus based formalisms is an entry barrier to its use by children.
• Their approach was to provide students with a software-based conceptual modelling environment that supports the articulation and qualitative simulation of their knowledge of physical systems.
• To guide students through the modelling process they have implemented coaching supports in response to student questions.
• Many graphical external representations have been created to aid students in articulating their understandings of phenomena. They can be grouped into three families, namely concept map notation, dynamical systems notations (STELLA, Model-It), and argumentation environments. The vocabulary for causal maps is drawn from QPT.
• It was reported that it is extremely difficult to create software that detects whether or not arguments and models are well-formed.
• RoboTA (Forbus et al., 2004a; Forbus et al., 2004b): an architecture for colony of distributed agents aimed at supporting instructional tasks.
• RoboTA is in use for providing assistance and feedback to the users of CyclePad (Forbus et al., 1999). The built-in email facility allows users of CyclePad to send in their design and get feedback from a design coach (the CyclePad Guru) that runs as a RoboTA agent.
• A RoboTA colony has two kinds of agents: A central server process (the PostOffice) and course or application-specific agents (TA agents).
CyclePad for Science and
Engineering Education
Kenneth D. Forbus et al.
• CyclePad is an Articulate Virtual Laboratory (AVL) for learning
engineering thermodynamics by design. Its intention was to scaffold the design task that frees the student from the burden of the design.
• CyclePad is a new kind of software called “articulate software” (Forbus, 1997). Properties of articulate software include: be fluent, supportive, generative, and customizable.
• The domain knowledge is represented using techniques from qualitative physics (Forbus, 1984) and compositional modelling (Falkenhainer and Forbus, 1991).
• Its domain theory includes: Physical and conceptual entities, structural knowledge, qualitative knowledge, quantitative knowledge, modelling assumptions, assumption classes, and economic model (Forbus and Whalley, 1994; Forbus et al., 1998; Forbus et al., 1999; Forbus, 2001).
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Qualitative Models
in Ecology
Paulo Salles, Helen Pain, & Robert I.
Muetzelfeldt
• Salles et al. (1996) explored different approaches to model qualitatively the vegetation, dynamics of Brazilian cerrado, in order to assess their suitability to provide the domain-specific knowledge in tutoring systems. The ultimate goal is to predict and explain the behaviour of ecology systems in qualitative terms.
• Two formalisms, the System of Interpretation of Measurements, Analysis and Observations (SIMAO) and the Qualitative Process Theory (QPT), are compared. The comparison aspects are: (1) capacity for making predictions about the behaviour of a plant population, and (2) the generation of explanations from encoded knowledge.
• Both SIMAO and QPT-based models can produce similar predictions to those obtained with a numerical model of the same problem. SIMAO provides a useful qualitative algebra to make calculations with heterogeneous variables. However it is not possible to incorporate descriptions of the ecological components nor do dynamic simulations with the SIMAO-based model.
• On the other hand, QPT allows the encoding of qualitative knowledge and building more detailed models, but does not provide a qualitative algebra for combining empirical values of variables.
• They also discussed the role of different organisational levels and scales of space and time in explaining the behaviour of ecological systems. A combined approach was said to be advantageous in building tutoring systems.
Ecological Modelling
Paulo Salles, Bert Bredeweg
S. Araújo & M. Neumann
• In the work reported by (Salles and Bredeweg, 1997), GARP was used as the qualitative simulation tool for modelling cerrado vegetation and community. Later, they moved on to implement works on ecological for nutrient cycling, stream ecosystem recovery, and community ecology applications.
• Initially, they hand-coded qualitative models for running in GARP. Then, Salles & Bredeweg (2001) investigated how to decompose a large qualitative simulation into a progressive sequence of smaller simulations, useful for teaching purposes, in the domain of ecology. The work discusses progressive learning routes through large qualitative simulation models of ecological systems using ideas on model dimensions from Causal Model Progression (CMP), the Genetic Graph (GG), and the Didactic Goal Generator (DGG).
• The group perceive modelling is a learning activity, an important educational activity (Salles and Bredeweg, 2002; Salles and Bredeweg, 2003; Salles and Bredeweg, 2006; Salles et al., 2003; Salles et al., 2006)
Qualitative Reasoning
in Interactive Learning
Environments
Paulo Salles, Bert Bredeweg
& Radboud Winkels
The work presented insights to the following aspects: • Finding the minimum set of model fragments needed for
simulating the behaviour of a system and to answer adequately a specific question.
• Generating specific trajectories of possible behaviours rather than returning a full prediction of all possible behaviours of a system.
• Providing the system with fault model that reflect common misconceptions.
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Qualitative Reasoning in Tutoring Interactions
Kees de Koning, Bert Bredeweg &
D.S. Weld
• The research done by de Koning and Bredeweg (1994, 1998) presented an experimental study that examines to what extent existing qualitative reasoning representations and techniques are sufficient for modelling the interaction between a student and a teacher when discussing the (qualitative) behaviour of physical devices.
• They investigated the usefulness of these models in actual teaching situations.
• Qualitative models are claimed to be beneficial for teaching systems.
• They claimed QR should be viewed not only as a means but also as goal, and that the knowledge representations as used in qualitative reasoning are largely adequate, whereas the reasoning techniques need adaptation for teaching.
• They also highlighted that, two important aspects are missing from early days of intelligent tutoring systems: (1) there is no representation of causality, and (2) there is no representation of physical structure (topological structure). Both the above are important for explanation though.
Tutoring System Dealing with
Physics
Kees de Koning et al.
• Main results and findings are described in (de Koning and Bredeweg, 1998; de Koning and Bredeweg, 1994; de Koning et al., 2000) are that:
• A framework is presented that defines a key role for qualitative models as interactive simulations of the subject matter. The framework focuses on automating the diagnosis of learner behaviour. Automated handling of tutoring and training functions in educational systems requires the availability of articulate domain models.
• They showed how a qualitative simulation model of the subject matter can be reformulated to fit the requirements of general diagnostic engines such as GDE.
• A set of procedures is presented that automatically maps detailed simulation models into a hierarchy of aggregated models by hiding non-essential details and chunking chains of causal dependencies. The result is a highly structured subject matter model that enables the diagnosis of learner behaviour by means of an adapted version of the GDE algorithm.
• An experiment has been conducted that shows the viability of the approach taken, i.e., given the output of a qualitative simulator the procedures they developed automatically generate a structured subject matter model and subsequently use this model to successfully diagnoses learner.
GARP Bert Bredeweg
• GARP is a domain independent qualitative reasoning engine implemented in SWI-Prolog – the PhD work of Bert Bredeweg (Bredeweg, 1992).
• In GARP, models are built using text editor and its interface is also text-based. The output of GARP consists of statements in predicate logic format, is not easy to understand for novices.
• The reasoning engine follows a compositional approach and implements the characteristics of QR technology by de Kleer & Brown (1984) and Forbus (1984)
HOMER
Vania Bessa Machado &
Bert Bredeweg
• HOMER (Bessa Machado and Bredeweg, 2002; Bessa Machado and Bredeweg 2003) implements a graphical oriented model building environment to GARP.
• In HOMER, The task of building a qualitative model is to create a set of model fragments (stored in a library) and specify one or more scenarios. A scenario refers to a structural description of the system.
235
QUAGS
F. Goddijn, Anders Bouwer,
& Bert Bredeweg
• Models constructed with HOMER can be exported as a set of files which can be used as input for GARP.
• When the simulator is called, it uses the model fragments to predict the behaviour of the system defined in the selected scenario.
• There is a support module that can guide the users through the model building process.
• QUAGS – stands for Questions about GARP Simulations. • QUAGS software generates questions based on simulations
produced by GARP (Goddijn et al., 2003).
VisiGarp
WiziGarp
Anders Bouwer & Bert Bredeweg
• VisiGarp (Bouwer, 2005; Bouwer and Bredeweg, 2005, Bouwer and Bredeweg, 2002; Bouwer and Bredeweg, 2001) implemented a graphical interface to GARP.
• The system allows users to inspect qualitative simulation models by interacting with automatically generated visualizations.
• The work investigates how explanations of dynamic phenomena can be generated using qualitative simulations and how these can be utilized in a domain independent interactive learning environment.
• The potential of aggregation principles to reduce the complexity of qualitative simulations has been explored.
• A prototype interactive learning environment has been implemented, called WiziGarp, which incorporates the aggregation mechanisms and expands the communicative functions of VisiGarp (Bouwer, 2005).
TCME
EBS
Tsukasa Hirashima
& Tomoya
Horiguchi
• Hirashima & Horiguchi (2009) proposed a method for semi-automating the description of graph of microworld using the compositional modelling mechanism.
• The Tiny Compositional Modelling Engine (TCME) generates the model of a given situation. The models are then used in a simulation-based learning environment. The aim of this research is to support adaptive learning with microworlds.
• In another work, Error-Based Simulation (EBS) is used as a method to visualize an erroneous equation in a mechanical problem (in physics). The approach was evaluated using QSIM (Kuipers, 94) and DQ-analysis (Weld, 1988). First, the EBS-manager predicts qualitative behaviour of the EBS by using qualitative simulation and compares it to a normal simulation. When a qualitative difference is found, the EBS-manager judges that the EBS is effective for error visualization. The EBS-manager also tries to find parameters by using comparative analysis of which perturbation cause qualitative differences between the EBS and a normal situation. After deriving the sequence of qualitative states based on an erroneous equation by QSIM, the EBS-manager derives the sequence of qualitative directions corresponding to the sequence qualitative states with perturbation of a parameter by using DQ-analysis.
The Teachable Agents
Betty’s Brain
K. Leelawong et al.
• The Teachable Agents project at Vanderbilt University (Biswas et al., 2001) showed a good example of how qualitative modelling can be useful for students.
• Their Betty’s Brain system uses qualitative representations expressed in concept maps to foster learning.
• The work extended intelligent learning environments with teachable agents to enhance learning. Their qualitative modelling framework uses qualitative mathematics, with tables for composing discrete values to provide qualitative simulation
236
• The task they use is to “teach” Betty (their software) by building concept maps so that Betty can produce explanations. This system turned out to be intensely motivating for students (Leelawong et al., 2001; Leelawong et al., 2003; Biswas et al., 2001)
ALI
(Automated Lab Instructor)
Aaron A. D’Souza
et al.
• ALI (D’Souza et al., 2001), a tool that uses qualitative representations to coach students while they interact with a virtual laboratory. ALI is based on the qualitative process theory (Forbus, 1984) and uses visual representations of direct influences and indirect influences.
• Course authors provide ALI the qualitative knowledge relevant to a specific quantitative model. When the quantitative model is simulated, ALI automatically infers the applicable causal dependencies and uses them to interact with the learner, both in terms of asking questions and showing graphics. A pilot study suggests that ALI does provide important guidance during discovery learning.
• ALI has a representation of the key relationships in the simulation model that the student should learn and it uses this knowledge to interleave its teaching opportunistically with the student's own discovery learning. Specifically, it can recognize learning opportunities in a student's experiments, test the student's resulting understanding, and gently guide the student towards these learning opportunities when necessary. ALI is claimed to be domain independent so that it can be attached to any quantitative simulation.
SOPHIE Project
J.S. Brown, D. Burton &
Johan de Kleer
• SOPHIE (de Kleer and Brown, 1992; Brown and Burton, 1982) is a big project that centred around three different SOPHIE systems (I, II and III).
• SOPHIE is a project focused on troubleshooting DC power supply. It can be seen as a pioneer landmark in trying to have computers communicate knowledge about the system behaviours with students.
• SOPHIE I & SOPHIE II included numerical simulation capabilities to create an artificial lab, or reactive learning
environment, in which a learner can perform experiments safely and easily and receive informed feedback, for the domain of troubleshooting of electronic circuits.
• With SOPHIE III, its designers incorporated a qualitative simulator, in an attempt to move towards more humanlike reasoning and explanation capabilities. A remarkable feature of the SOPHIE systems was the robust natural language interface, which could handle a broad range of queries from a user.
A Framework for High School Level
Mathematics
Walther Neuper & Franz Wotawa
• Neuper (2001) and (Neuper and Wotawa, 2002) presented a framework for handling knowledge base on Model Based Reasoning (MBR).
• The work constructs mathematical model from textual description. The basic element used in the framework is a “mathematical concept”. Its semantics is based on first order logic.
• However, not all mathematical examples can be expressed within the framework.
• Moreover, guidance is not provided in the modelling phase and generation of explanation is only possible in modelling phase.
237
Appendix B
Collection of Flowcharts for the Qualitative Reasoning Framework
238
Figure B.1 Workflow of the QPT-based modelling, reasoning and explanation framework.
239
Substrate Recognizer (Function No. 1)
Enter
Recognize structural units
in organic substrate
Identify structural units as
nucleophiles or
electrophiles
Prepare view pairs based
on recognized types for
each individual
Exit
Pairs of
individuals
Chemical
KB
View
Instance
Structure
(VIS)
Figure B.2 The task performed by the “Substrate Recognizer”.
240
Model Constructor (Function No. 2)
Enter
Retrieve the suggested
chemical process
Make-bond?
Store the chemical theories
in special purpose arrays
(in the format of QPT slot)
Retrieve the chemical data
and theories implicit in the
identified bond activity
Exit
Break-bond?
yes
no no
yes
Figure B.3 Workflow for automating QPT model for organic processes.
241
Qualitative Simulator (Function No. 3)
Enter
Exit
Check process's
quantity
Quantities that are directly
influenced by a process; termed
as "direct influence" in QPT
QSA
Examine indirect
influences and their effect
propagation
Process
entry_conditions
violated
?
Yes
No
Enter
Exit
Store direct influenced quantity
in special purpose array
Check qualitative
proportionalities in "Relation"
slot of the process
Store propagated effects in
special purpose arrays
This step helps provide
causal graph generation
This step helps answering
the how? why? what?
types of question
regarding a process
simulation and behaviour
So that the current
process may stop & the
next process may start, if
there are reactive
individuals left in the VIS
The QSA routine
Store new individuals in VISVIS
New individuals such
as the intermediates
produced in each
small reaction step
Figure B.4 Workflow of the QPT-based simulation and the micro steps in the QSA module.
242
Enter
Check Qty-Cond to see
which entry
requirement is violated
Why does a process
start/stop?
What are the causes
for a particular quantity
to assign a new value?
Why some structural
units left the main
compound?
How a particular
type of intermediate
product is obtained?
Exit
Select a question from
the pull-down list
Check what new
individuals are created
during a process
reasoning
Check quantity spaces
for affected individuals
Check all functional
dependencies starting
from the direct influence
slot of a QPT process
Display answers
accordingly
yes
yes
yes
yes
no
no
no
no
Explanation Generator (Function No. 4)
Figure B.5 Workflow of the technique used in handling and generating an explanation.
243
Appendix C
Questionnaires Used for Collecting Students’ Feedback on
the use of QPT and Qualitative Reasoning Approaches
244
PART A: Your understanding towards the QPT.
Directions:
To answer this questionnaire, take your time to really get into the mood of the
situation. When rating the statement below, please give your opinions based on what
you actually ‘experienced’ while studying the QPT way of modelling the organic
reaction and SN1 mechanism. Indicate the extent to which you agree or disagree with
each statement by circling the number that best express your opinion.
Strongly
Disagree
Disagree Neither
Agree Nor Disagree
Agree
Strongly Agree
1 The identification of quantities
(parameters) helped me to establish
the functional dependency among
them.
1 2 3 4 5
2 The specification represented using
QPT makes it easy to understand the
organic processes (reaction steps) that
are involved in a chemical reaction
simulation.
1 2 3 4 5
3 The various slots in a QPT process
make me confused. 1 2 3 4 5
4 The flow of the reasoning is more
systematic when a QPT specification
that captures the chemical knowledge
and intuition is given.
1 2 3 4 5
5 I still don’t know how to read a QPT
model even though it is already taught. 1 2 3 4 5
6 The specification describes almost
exactly what I have in mind. 1 2 3 4 5
7 There are still many concepts implicit
in the reaction formula but I don’t
seem to see them in the model.
1 2 3 4 5
Figure C.1 Questionnaire to assess students’ understanding on QPT.
245
PART B: Your opinion about applying qualitative modelling and reasoning in the
teaching and learning organic reaction mechanisms.
Directions:
Based on the short lecture about qualitative reasoning and modelling using QPT for
learning SN1 reaction, please indicate (by circling) on the scale below, your opinion
about the appropriateness and effectiveness of applying qualitative in the said
domain.
Strongly
Disagree
Disagree
Neither
Agree Nor
Disagree
Agree
Strongly
Agree
1 The likelihood that I would read
further about qualitative reasoning &
modelling is high.
1 2 3 4 5
2 I don’t like the trouble of going
through modelling and simulation
before real experiment.
1 2 3 4 5
3 If I want to explain reaction
mechanisms to my friends, this is the
type of formal way that I’m looking for.
1 2 3 4 5
Please indicate, in general, how favourable you are with the qualitative way of
modelling and explaining the reactions and its mechanism.
Strongly
Favourable
Favourable
Neither Favourable
Nor Unfavourable
Unfavourable
Strongly
Unfavourable
1 2 3 4 5
If you wish to make any suggestions regarding any of the points covered in this
questionnaire, please use the space provided.
--------------------------------------------------------------------------------------------------------- --------------------------------------------------------------------------------------------------------- --------------------------------------------------------------------------------------------------------- ---------------------------------------------------------------------------------------------------------
Figure C.2 Questionnaire to collect students’ opinions on qualitative modelling and reasoning approaches of problem solving for organic chemistry.
246
Appendix D
Selected Computer Screenshots
247
Figure D.1 Login page.
Figure D.2 Front page of the QRiOM qualitative simulator.
248
Figure D.3 Main interface of QRiOM.
Figure D.4 More learning activities and explanation can be viewed by clicking A, B and C buttons.
249
Figure D.5 Reaction route for the simulation of “CH3Cl + HO−”.
Figure D.6 Reaction route for the simulation of “CH3CH3CH3Br + 2H2O”.
250
Figure D.7 Reaction route for “CH3CH3CH2Cl + HO−”.
Figure D.8 QPT model inspection page.
Learners may
inspect the
automated
models
251
Figure D.9 A“make-bond” process described in QPT terms (between a charged nucleophile and a charged electrophile).
Figure D.10 A causal graph showing the cause and effect relationships of the various chemical parameters during qualitative reasoning.
252
Figure D.11 Causal graph inspection page with annotation.
Figure D.12 Brief explanation of each slot in a QPT model is provided.
253
Figure D.13 More explanation for the various modelling constructs of QPT.
Figure D.14 Contents of the View Instance Structure (VIS) give the pairs of reacting species used in each small reaction step.
254
Figure D.15 A snapshot of the contents of the VIS during the simulation of “CH3CH3CH3COH + HBr”.
Figure D.16 Each chemical state change (parameter state history) is recorded for further examination.
Learners can select
any reacting species
(views) to study its
parameter history
255
Figure D.17 Chemical states for “HO−”are retrieved and displayed.
Figure D.18 Contents in the “substrate table” showing the functional units involved in a reaction.
256
Figure D.19 The screenshot for a specific case where QRiOM is unable to predict the output, where the reason is displayed via a pop-up window.
Figure D.20 A screenshot of “no reasoning” for an input pair of <CH3Cl, HF>, where the system simply returns a short message.
257
Figure D.21 A QPT learning corner is included in the software.
Figure D.22 A “terminology help window” that provides quick notes for important organic chemistry terms used in simulation and explanation.
258
Figure D.23 The main interface for “model building” by the students – for future expansion of the simulator.
Figure D.24 Knowledge base Editor – for adding/deleting chemical facts and theories.
259
Appendix E
Program Snippets for the Main Software Modules in QRiOM
260
Program Snippets for Views and Processes Constructors
Figure E.1 shows the Java instructions that retrieve the chemical facts from the KB
(stored as Prolog clauses) and to prepare slots for a QPT model. The chemical
information will be used to compose QPT processes. Note that texts after the “//” sign
are comments.
: amzi.ls.LogicServer ls = new amzi.ls.LogicServer(); long term3, term4, term5; globalDataEG gd = new globalDataEG(); step3_Nu = TwoViewStructureArr[1]; // E.g. Cl- String tempElectrophile = TwoViewStructureArr[0]; // E.g. C+ try{ ls.Init(""); ls.Load("chemkb.xpl"); // Connect to Prolog backend file term3 = ls.ExecStr("nucleophile('"+step3_Nu+"', Q)"); // Retrieve the required data for a nucleophile if (term3 == 0){ ls.Close(); return; } gd.TypeList1[trying] = nuStart[trying] = ls.GetStrArg(term3, 2); term4 = ls.ExecStr("find_reacting_unit(intermediateProd, tempElectrophile, P , _)"); if (term4 == 0){
ls.Close(); return;
} step3_Elec = ls.GetStrArg(term4, 3); term5 = ls.ExecStr("electrophile('"+step3_Elec+"', R)"); // Retrieve the required data for an electrophile if (term5 == 0){ ls.Close(); return; } gd.TypeList2[trying] = electroReagent[trying] = ls.GetStrArg(term5, 2); ls.Close(); } // end try catch (Throwable t) { t.printStackTrace(); }
(a) Retrieving essential chemical information of the individuals involving in a chemical process
261
public void viewModel_actionPerformed(ActionEvent e) { amzi.ls.LogicServer ls = new amzi.ls.LogicServer(); : String pn = gd.ProName; String ind1 = gd.View1; String Ind1Type = gd.Type1; String Ind2Type = gd.Type2; String ind2 = gd.View2; : jTextArea2.setFont(new java.awt.Font("Dialog", Font.BOLD, 12)); jTextArea2.append("Process Activated: " + gd.ProNameList[0] + "\n"); jTextArea2.append("\n" + "Individuals (The reacting units in this process/step)" + "\n"); jTextArea2.setFont(new java.awt.Font("Dialog", Font.PLAIN, 10)); jTextArea2.append(" " + gd.ViewList1_sn2[0] + "\t" + gd.ViewList2_sn2[0] + "\n"); jTextArea2.setFont(new java.awt.Font("Dialog", Font.BOLD, 12)); jTextArea2.append("\n" + "Quantity-Condition (Entry requirements to activate the process)" + "\n"); jTextArea2.setFont(new java.awt.Font("Dialog", Font.PLAIN, 10)); : ls.Load("bondKB.xpl"); term1 = ls.CallStr("qty_cond("+gd.ProNameList[0]+", "+gd.TypeList1[0]+", X, Y, Z).");
: do { jTextArea2.append(" " + ls.GetStrArg(term1, 3) + "(" + ls.GetStrArg(term1, 4) + ") "
+ ls.GetStrArg(term1, 5) + "\n" ); } while (ls.Redo()); term2 = ls.CallStr("qty_cond("+gd.ProNameList[0]+", "+gd.TypeList2[0]+", X, Y, Z)."); : do{ jTextArea2.append(" " + ls.GetStrArg(term2, 3) + "(" + ls.GetStrArg(term2, 4) + ") " + ls.GetStrArg(term2, 5)+ "\n" ); } while (ls.Redo()); jTextArea2.setFont(new java.awt.Font("Dialog", Font.BOLD, 12)); jTextArea2.append("\n" + "Infuences (Direct effect caused by the process)" + "\n"); jTextArea2.setFont(new java.awt.Font("Dialog", Font.PLAIN, 10)); if (gd.ProNameList[0].equals("make_bond")) jTextArea2.append(" " + "A covalent bond is added (formed)" + "\n"); else jTextArea2.append(" " + "A covalent bond is removed (cleaved)" + "\n"); jTextArea2.setFont(new java.awt.Font("Dialog", Font.BOLD, 12)); jTextArea2.append("\n" + "Parameters dependency (Effects propagation)"); jTextArea2.setFont(new java.awt.Font("Dialog", Font.PLAIN, 10)); : do{ jTextArea2.append(" " + ls.GetStrArg(term3, 3) + "(" + ls.GetStrArg(term3, 5) + ") followed by " + ls.GetStrArg(term3, 4) + "(" + ls.GetStrArg(term3, 6) + ")" + "\n"); } while (ls.Redo()); jTextArea2.append("\n" + gd.ViewList2[0] + " [" + gd.TypeList2[0] +"]" + ":" + "\n"); term3 = ls.CallStr("process_relations(make_bond, "+gd.TypeList2[0]+", P, Q, R, S)."); : do{ jTextArea2.append(" " + ls.GetStrArg(term3, 3) + "(" + ls.GetStrArg(term3, 5) + ") followed by " + ls.GetStrArg(term3, 4) + "(" + ls.GetStrArg(term3, 6) + ")" + "\n"); } while (ls.Redo()); : }
Get ready the individuals for the chemical process
Based on the view’s type,
general set of chemical
theories are retrieved from
the KB
Display the effect
propagation caused by the process. These are the indirect influences of a
QPT model
Prepare the headings for the QPT model, and
the direct influence of the
process
Prepare the individuals for the chemical process
(b) Preparing QPT slots for displaying on the GUI
Figure E.1 The Java code for retrieving chemical facts of reacting species and for constructing a QPT process.
262
Program Snippets for Prediction Engine
The Quantity Space Analyzer (QSA) is one the most important software modules in the
reasoning engine of QRiOM. The module will be called up to perform tasks such as
updating and maintaining multiple data structures whenever an organic process is
determined based on the view pairs. The Java codes for “view structure updating” and
“atom properties updating” submodules in the QSA will be shown in the next few
subsections. These two submodules collectively help predict the final product of a
reaction simulation.
View structure updating: Figure E.2 shows the main processing steps in keeping track
of the updated status of the VIS. The contents of the array can be used to suggest the
next chemical process for reasoning.
: if (Subst_1_ChargedHistory[4].equals("pos")) { appendedCharge1 = StartMaterialTable[0].concat("+"); // Check its charge’s state theStr1 = "CH3CH3CH3".concat(appendedCharge1);} else if (Subst_1_ChargedHistory[4].equals("neg")) { appendedCharge1 = StartMaterialTable[0].concat("-"); theStr1 = "CH3CH3CH3".concat(appendedCharge1);} else theStr1 = "CH3CH3CH3".concat(StartMaterialTable[0]); if (Agent_2_ChargedHistory[4].equals("pos")) { appendedCharge1 = StartMaterialTable[1].concat("+"); theStr2 = theStr1.concat(appendedCharge1);} else if (Agent_2_ChargedHistory[4].equals("neg")) { appendedCharge1 = StartMaterialTable[1].concat("-"); theStr2 = theStr1.concat(appendedCharge1);} else theStr2 = theStr1.concat(StartMaterialTable[1]); ViewStructureArr[0] = theStr2; // Update the contents of VIS
:
Figure E.2 The associated Java statements for updating the VIS in order to suggest the next organic process in the qualitative simulation environment.
263
Atom property updating: Figure E.3 presents the code in updating the chemical states of
atoms during simulation. The contents of the special tables will be used to generate
causal graphs, display the atom property table and produce the parameter history table.
: ls.Init(""); ls.Load("chemkb.xpl"); t1 = ls.CallStr("qpropAPTable(make_bond, chargedElec, P3, Q3, R3, S3); // Prepare to retrieve chemical theories if (t1 == 0) { : ls.Close(); return; } do{ if (n1 == 1){ qtyArrTemp[0] = ls.GetStrArg(t1, 3); qtyArrTemp[1] = ls.GetStrArg(t1, 4); signArrTemp[0] = ls.GetStrArg(t1, 5); signArrTemp[1] = ls.GetStrArg(t1, 6);} if (n1 == 2){ qtyArrTemp[2] = ls.GetStrArg(t1, 3); qtyArrTemp[3] = ls.GetStrArg(t1, 4); signArrTemp[2] = ls.GetStrArg(t1, 5); signArrTemp[3] = ls.GetStrArg(t1, 6);} n1++; } while (ls.Redo()); if ((individual_2_type.equals("chargedElec")) && (bActivity.equals("make_bond"))) { if ((signArrTemp[0].equals("plus")) && (qtyArrTemp[0].equals("charge"))) { for (int t=0; t<=2; t++) if (charge[t].equals(smt_0_ChargeVal)) index = t; gdat.Sub_1_ChargedHistory[4] = Subst_1_ChargedHistory[4] = charge[++index]; smt_0_ChargeVal = Subst_1_ChargedHistory[4];} else if ((signArrTemp[0].equals("minus")) && (qtyArrTemp[0].equals("charge"))) { for (int t=0; t<=2; t++) if (charge[t].equals(smt_0_ChargeVal)) index = t; gdat.Sub_1_ChargedHistory[4] = Subst_1_ChargedHistory[4] = charge[--index]; smt_0_ChargeVal = Subst_1_ChargedHistory[4];} if ((signArrTemp[0].equals("plus")) && (qtyArrTemp[0].equals("lone_pair_electron"))) { for (int t=0; t<=4; t++) if (String.valueOf(lone_pair[t]).equals(smt_0_LPVal)) index = t; gdat.Sub_1_LonePairHistory[4] = Subst_1_LonePairHistory[4] = String.valueOf(lone_pair[++index]); smt_0_LPVal = Subst_1_LonePairHistory[4];} else if ((signArrTemp[0].equals("minus")) && (qtyArrTemp[0].equals("lone_pair_electron"))) { for (int t=0; t<=4; t++) if (String.valueOf(lone_pair[t]).equals(smt_0_LPVal)) index = t; gdat.Sub_1_LonePairHistory[4] = Subst_1_LonePairHistory[4] = String.valueOf(lone_pair[--index]); smt_0_LPVal = Subst_1_LonePairHistory[4];} if ((signArrTemp[0].equals("plus")) && (qtyArrTemp[0].equals("no_of_bond"))) { for (int t=0; t<=4; t++) if (String.valueOf(bond[t]).equals(smt_0_BondVal)) index = t; gdat.Sub_1_BondHistory[3] = Subst_1_BondHistory[3] = String.valueOf(bond[++index]); smt_1_BondVal = Subst_2_BondHistory[4];} else if ((signArrTemp[0].equals("minus")) && (qtyArrTemp[0].equals("no_of_bond"))) { for (int t=0; t<=4; t++) if (String.valueOf(bond[t]).equals(smt_0_BondVal)) index = t; gdat.Sub_2_BondHistory[4] = Subst_2_BondHistory[4] = String.valueOf(bond[--index]); smt_0_BondVal = Subst_1_BondHistory[4];} : : }
The chemical theories (stored as qprop) for the identified organic
process in the chemical KB are
retrieved and stored in
temporary arrays
The states of the chemical
parameter are updated based
on the qualitative
proportionalities retrieved earlier.
Figure E.3 The Java code for updating the chemical parameters’ states of each atom during simulation.
264
Program Snippets for Causal Graph Generator
Figure E.4 shows the code fragment that generates a causal graph by using the entries in
various special purpose data structures discussed in Chapter 5.
public void causalGraph_actionPerformed(ActionEvent e) { globalDataEG gd = new globalDataEG(); jCG.setText(" "); jCG.append("\nThis is the causal diagram for the reaction formula you just selected\n"); if (gd.procInvolved[0].equals("make_bond")){ jCG.append("Step 1: Make-Bond Process\n"); jCG.append("Nucleophile" + "(" + gd.smn + ")" + "\t\t\t" + "Electrophile" + "(" + gd.aat00 + ")" +"\n" ); for(int y=0; y<=2; y++){ if(!(gd.par1[y].equals("nil"))) if (y==1) jCG.append("\t\t" + gd.par1[y] + "(" + gd.sign1[y] + ")" ); else jCG.append("\t\t" + gd.par1[y] + "(" + gd.sign1[y] + ")" ); else jCG.append("\t\t\t\t"); if(!(gd.par2[y].equals("no change"))) { if (y==2) jCG.append("\t\t\t" + gd.par2[y] + "(" + "no change" + ")" + "\n"); else if (y==1) jCG.append("\t" + gd.par2[y] + "(" + gd.sign2[y] + ")" + "\n"); else jCG.append("\t\t" + gd.par2[y] + "(" + gd.sign2[y] + ")" + "\n");} else jCG.append("\n"); } : : } // End Generate Causal Graph
These codes prepare the values taken by each main
parameter during the entire reasoning and simulation. The set of values are then formatted onto the appropriate user
graphical interfaces
Figure E.4 The Java statements for constructing a causal graph.
Parameter History Maintenance and Retrieval: The construction of causal models
and the production of 2D organic structure would require referencing the contents of the
so-called “parameter history” structure. The Java code for retrieving the parameter
history of a given reacting unit is presented in Figure E.5.
265
: if (jComboBox1.getSelectedIndex() == 0){ // if (unitNameSelected.equals("gd.AAT[0]")){ jTextArea2.append("\n" + "Charge History:" + "\n"); jTextArea2.append("Initial State " + " State After Step 1 " + " State After Step 2 " + " State After Step 3 (Final State)" + "\n"); for (int p=1; p<=4; p++) { if (gd.Agen_1_ChargedHistory[p].equals(" ")) gd.Agen_1_ChargedHistory[p] = "nil"; jTextArea2.append("[" + gd.Agen_1_ChargedHistory[p] + "]"); jTextArea2.append(" "); } jTextArea2.append("\n\n" + "Covalent Bond History:" + "\n"); jTextArea2.append("Initial State " + " State After Step 1 " + " State After Step 2 " + " State After Step 3 (Final State)" + "\n"); for (int p=1; p<=4; p++) { if (gd.Agen_1_BondHistory[p].equals(" ")) gd.Agen_1_BondHistory[p] = "nil"; jTextArea2.append("[" + gd.Agen_1_BondHistory[p] + "]"); jTextArea2.append(" "); } jTextArea2.append("\n\n" + "Lone Pair History:" + "\n"); jTextArea2.append("Initial State " + " State After Step 1 " + " State After Step 2 " + " State After Step 3 (Final State)" + "\n"); for (int p=1; p<=4; p++) { if (gd.Agen_1_LonePairHistory[p].equals(" ")) gd.Agen_1_LonePairHistory[p] = "nil"; jTextArea2.append("[" + gd.Agen_1_LonePairHistory[p] + "]"); jTextArea2.append(" "); } jTextArea2.append("\n\n" +"Note: \"nil\" means \"no reaction\" in the step. "); }
Figure E.5 The Java statements for retrieving the parameter history of a reacting unit.
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Program Snippets for Knowledge Validation
A representative set of definitions in OntoRM is given in Figure E.6.
public class ontoRM {
// Public data: the attributes that are common to all reacting units (nu/elec/LG) String hasNameRM = ""; String hasTypeRM = ""; int hasBondNumberRM =-1; String hasChargeStateRM = ""; : String hasSuggestedMechanism; String [] processUsedRM = {"", "", ""}; // For (NUCLEOPHILE) & (ELECTROPHILE) char hasElectroNegativityRM; // GREATER_LESSER the neigboring atom String hasRsdegreeRM; // [primary, secondary, tertiary] String hasCarbocationStabilityRM; // [no, yes] boolean hasAbilityAsLeavingGroupRM; // [0, 1], for checking whether is a weak base (=stable ion) String hasNucleophilicity; // [low, high] // For (LG) String hasDegreeSubstituentRM; // [primary, secondary, tertiary] String hasAtomAttachmentTypeRM; // ATOM_ATTACH_TYPE (carbon/oxygen) String hasBaseStrengthRM; // [weak, strong], used by sn1/sn2 to see whehter can break bond String hasBondTypeRM = ""; // [single, double, triple, ring] String hasElectroNegativityRM; // [>, <] // For (Substrate) String hasFunctionalGroupNameRM; String hasFunctionalGroupTypeRM; int hasCarbonDegMainChainRM; // FOR ALCOHOL AND ALKYL HALIDE String hasBondTypeRM; // [single, double] String hasDegreeSubstituentBearingFuncUnitRM; // [primary, secondary, tertiary] String hasLGTypeRM; // E.g. "OH" - ok, "Cl" - ok, "F" - no reaction (strong base, too reactive) String hasLGNameRM; // FOR SN1 AND SN2 MECHANISMS String [] hasPatternOfReactantsRM; //E.g. how many bonds (at LG location), how many Rs (just 3 and 2 for sn1), what type of bonds String hasAliasRM; // full name of sn1 and sn2 respectively String [] hasReactantNamesRM; // hydrogen chloride String [] hasSubstrateNamesRM; // tertiary alcohol String [] hasEndProductsRM; // water, alkyl halide, alcohol String [] hasRateDetermineStepRM; // e.g. the dissociate step determines the reaction rate String [] hasProcessOrderRM; // e.g. [make_bond, break_bond, make_bond] int hasAllowedDegreeOfCarbonRM; // [1,2,3] int hasDegreeSubstituentMainCarbonRM; // [1,2,3]
: String [] [] hasViewPairConstraintRM = {{"make_bond", "neutralNu", "chargedElec"}, {"make_bond", "chargedElec", "chargedNu"}, {"make_bond", "chargedNu", "neutralElec"}, {"break_bond", "neutralElec", "chargedNu"}, {"break_bond", "neutralElec", "neutralNu"} }; String [] hasSpecialCauseRM = {"nucleophilicity", "temperature", "pH", "Solvent Type", "LG type", "Alkyl halide used"}; // Default Constructor : // Other Constructor public ontoRM(String theViewName, String theViewType, int theBondNo, String theChargeState) { hasNameRM = theViewName; hasTypeRM = theViewType; hasBondNumberRM = theBondNo; hasChargeStateRM = theChargeState;} : }
(a) Representative set of attributes for constituents of organic compounds and the definitions of general chemical properties of reaction mechanisms.
267
: functional_unit(nucleophile). functional_unit(electrophile). rm(organic_mechanism). is_a(chargedNu, nucleophile). is_a(neutralNu, nucleophile). is_a(chargedElec, electrophile). is_a(neutralElec, electrophile). is_a(nucleophilic_substitution, organic_mechanism). is_a(elimination, organic_mechanism). is_a(electrophilic_addition, organic_mechanism). example_of(chloride_ion, chargedNu). example_of(alcohol_oxygen, neutralNu). example_of(carbon, neutralElec). example_of(hydrogen_ion, chargedElec). example_of(carbocation, chargedElec). example_of(sn1, nucleophilic_substitution). example_of(sn2, nucleophilic_substitution).
:
(b) Hierarchical structuring of the basic concepts of reaction mechanisms (in Prolog syntax)
Figure E.6 A sample set of definitions for nucleophiles, electrophiles and the basic concepts of organic mechanisms.
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Knowledge Validation Implemented as Java Methods
Figure E.7 shows the Java method that checks whether two nucleophiles are from the
same group in the periodic table. If so, there will be a nucleophilic substitution.
Otherwise, a message of “no reaction” will be suggested. By doing so, the qualitative
simulator will not simply carry out reasoning when in actual case none is required.
Note that the checkNucleophilicity method makes use of the “hasNucleophilicity”
attribute of a NucleophileView concept in OntoRM. Figure E.8 validates if a substrate
can undergo SN1 at all. A pair of views and its suggested covalent bonding will also
undergo validation to make sure it is a valid organic process as presented in Figure E.9.
public int checkNucleophilicity(String theComingNu, String theCompoundNu){
int nu1Scale= -2, nu2Scale = -2, nuFlag = 0; String globalStr1="", globalStr2="", globalStr3 = ""; globalDataEG gNu = new globalDataEG(); : if (theCompoundNu.equals("F")) nu2Scale = nucleophilicity[0]; else if (theCompoundNu.equals("Cl")) nu2Scale = nucleophilicity[1]; else if (theCompoundNu.equals("Br")) nu2Scale = nucleophilicity[2]; else if (theCompoundNu.equals("I")) nu2Scale = nucleophilicity[3];
char[] tempNuArr = theComingNu.toCharArray(); tempNuArr[0] = ' '; String theComingNuTran = String.valueOf(tempNuArr); : if (nu1Scale > nu2Scale) { gNu.gStr1 = globalStr1 = "Since the nucleophilicity of " + concatNuStr + " is higher than " + theCompoundNu + " (resides in the compound)" ; gNu.gStr2 = globalStr2 = "Hence, " + concatNuStr + " (a better nucleophile) will replace " + theCompoundNu ; gNu.gStr3 = globalStr3 = "The product is : " + concatFinalProd; nuFlag = -3;}
else { gNu.gStr2 = globalStr2 = "Since the nucleophilicity of " + concatNuStr + " is lower than " + theCompoundNu + " (the nucleophilic center of the compound)" ; gNu.gStr3 = globalStr3 = "And, the "+ theCompoundNu + " is a poor LG, so the reaction is extremely slow."; gNu.gStr1 = globalStr1 = "The bond between the C and the " + theCompoundNu + " is difficult to break."; nuFlag = -4;} return nuFlag; }
Figure E.7 A Java method that checks the nucleophilic reactivity for a pair of nucleophiles for possible substitution.
269
public String noReactionCheck(String subst){ long term1; try { amzi.ls.LogicServer ls = new amzi.ls.LogicServer(); ls.Init(""); ls.Load("ontology.xpl"); term1 = ls.CallStr("no_reaction(X,'"+subst+"' ,Y)"); if (term1 == 0) { JOptionPane.showMessageDialog(null, subst + " not found!\n", "Substrate's reactivity check", JOptionPane.PLAIN_MESSAGE); ls.Close(); return "nil"; } ls.Close(); } catch (Throwable t) { t.printStackTrace(); } return "hasreaction"; } The Java method will require information from the chemical knowledge base, such as the following: no_reaction(sn2, 'CH3CH3CH3COH', 'Reason: Crowded'). no_reaction(sn1, 'CH3OH', 'Reason: Intermediate that produced is not stable'). no_reaction(sn1, 'CH3CH3CH3CF', 'Reason: Unstable ion when left the compound'). non_reactive_substrate('CH4'). :
Figure E.8 A Java method that checks whether a substrate can undergo SN1 or SN2.
public String sameViewTypeCheck(String View1, String View2){ long term1, term2; String sv1="", sv2=""; try { amzi.ls.LogicServer ls = new amzi.ls.LogicServer(); ls.Init(""); ls.Load("ontology.xpl"); term1 = ls.CallStr("has_view_type('"+View1+"', X)"); if (term1 == 0) { ... ); : } term2 = ls.CallStr("has_view_type('"+View2+"', Y)"); if (term2 == 0) { JOptionPane.showMessageDialog(null, View2 + " is not found!\n", "View's type check", JOptionPane.PLAIN_MESSAGE); ls.Close(); return "false"; } sv1 = ls.GetStrArg(term1, 2); sv2 = ls.GetStrArg(term2, 2); ls.Close(); } catch (Throwable t) { t.printStackTrace(); } if (sv1.equals(sv2)) return "no_bond_activity"; else return "proceed_bond_activity"; }
Figure E.9 A Java method that checks the types of individual views in order to recommend a suitable chemical process.
270
Control Scheme to Stop the Entire Simulation
Figure E.10 gives the statements to end a simulation. Figure E.11 contains statements
to generate the sequence of organic processes used in the prediction of the outputs, and
Figure E.12 gives the statements to generate the final products.
: while((count != 1) && (flag == -1)) { if (first_process == 'm'){ viewPair vp = new viewPair(sn1_sequence1[proc_step]); gdat.ProNameList[step] = processInvolved[step] = sn1_sequence1[proc_step] vp.quantitySpaceAnalyzer(); proc_step++; step = step + 1;} else if (first_process == 'b'){ viewPair vp = new viewPair(sn1_sequence2[proc_step]); processInvolved[step] = sn1_sequence2[proc_step]; vp.quantitySpaceAnalyzer(); proc_step++; step = step + 1;} if (proc_step == 1) { gdat.OneVSA[0] = OneViewStructureArr[0] = ViewStructureArr[0]; gdat.OneVSA[1] = OneViewStructureArr[1]= ViewStructureArr[1]; gdat.OneVSA[2] = OneViewStructureArr[2]= ViewStructureArr[2]; } else if (proc_step == 2) { gdat.TwoVSA[0] = TwoViewStructureArr[0]= ViewStructureArr[0]; gdat.TwoVSA[1] = TwoViewStructureArr[1]= ViewStructureArr[1]; gdat.TwoVSA[2] = TwoViewStructureArr[2]= ViewStructureArr[2]; } else if (proc_step == 3) { gdat.ThreeVSA[0] = ThreeViewStructureArr[0]= ViewStructureArr[0]; gdat.ThreeVSA[1] = ThreeViewStructureArr[1]= ViewStructureArr[1]; gdat.ThreeVSA[2] = ThreeViewStructureArr[2]= ViewStructureArr[2]; for (int v=0; v<=ViewStructureArr.length-1; v++) if (ViewStructureArr[v] != " ") count++; } trying++;
: : }// end else if if ((Agent_2_ChargedHistory[4].equals("neutral")) && (Subst_1_ChargedHistory[4].equals("neutral"))) {flag = 1;} } // while-loop
This line checks whether the substituted
nucleophile and the ultimate product are in their stable states
(e.g. no charge around the atoms)
This line checks whether VSA is having just one
element (i.e. count = 1), if so then the
entire reaction may stop since the top-most loop condition
is violated.
Figure E.10 The associated Java statements to stop the entire reaction simulation.
271
for(int m=0; m<=2; m++)
jTextArea1.append(" " + processInvolved[m]);
This array is updated each time a new organic
process is recommended.
Figure E.11 The Java statements for displaying the organic processes in the order of occurring.
jTextArea3.append(" " + ThreeViewStructureArr[0] + " and "); for (i = 0; i <= IntermediateArr.length -1; i++) jTextArea3.append(IntermediateArr[i]);
The bottom most element of VSA stores the final
product, while other side products are stored in
intermediate array.
Figure E.12 The Java statements to display the final product.
