Artificial Intelligence - Basics, Market, Logic Programmingheiko-angermann.com/wp-content/uploads/2019/10/... · R. Gläß, Künstliche Intelligenz im Handel 2 – Efﬁzienz erhöhen

Artificial IntelligenceBasics, Market, Logic Programming

Dr. Heiko Angermann1

1Baden-Wuerttemberg Cooperative State University (Heidenheim)

October 1, 2019

Artificial Intelligence

Manuscript [1]

You can access the manuscript at the following URL:http://www.heiko-angermann.com/artificial-intelligence/

Dr. Heiko Angermann - Baden-Wuerttemberg Cooperative State University 2/312

http://www.heiko-angermann.com/artificial-intelligence


Manuscript [2]

On overview of all my lectures is presented at URL:http://www.heiko-angermann.com/teaching/


http://www.heiko-angermann.com/teaching/


Author Information [1]

Dr. Heiko AngermannPh.D. in Computer EngineeringB.Eng. in Print- and Media Technology

Contact Information (University):Adress: Marienstraße 20, Room 616, 89518 Heidenheim, GermanyE-Mail: [email protected]: +49 (0) 7321 2722 476


mailto:[email protected]


Author Information [2]

Contact Information (Personal):E-Mail: [email protected]: http://www.heiko-angermann.com

Contact Information (Social Media):LinkedIn: http://www.linkedin.com/in/heikoangermannXing: https://www.xing.com/profile/Heiko_Angermann


mailto:[email protected]

url:www.heiko-angermann.com

http://www.linkedin.com/in/heikoangermann

https://www.xing.com/profile/Heiko_Angermann


Table of Contents: [1]

Basics Artificial IntelligenceIntroduction to Artificial IntelligenceHistory Artificial IntelligenceAgents in Artificial IntelligenceAreas of Artificial IntelligenceOntologies and TaxonomiesSome Algorithms & Techniques for AIPractice - Basics of Artificial Intelligence



Table of Contents: [2]

Artificial Intelligence MarketGeneral relevance Artificial intelligenceIndustrial Applications Artificial IntelligenceRecent market situation Artificial IntelligencePractice - Applications of Artificial Intelligence

Logic Programming (Basics)Basics of Logical ProgrammingIntroduction to PrologConcepts of PrologBuilt-In PredicatesSome Prolog ExamplesPractice - Logic Programming (Basics)

References



Basic Literature [1]

The following publication is used as basic literature for this manuscript:

H. Angermann, N. Ramzan, Taxonomy Matching UsingBackground Knowledge – Linked Data, Semantic Web andHeterogeneous Repositories, Springer (2017), 103 p.



Basic Literature [2]

https://link.springer.com/book/10.1007/978-3-319-72209-2


https://link.springer.com/book/10.1007/978-3-319-72209-2


Other Literature [1]

Other and additional interesting literature that can be used tounderstand the subjects discussed in this manuscript:

H. Angermann, An adaptive approach for user-specific taxonomyevolution using background knowledge, Doctoral Thesis (2016),134 p.

W. Ertel, Grundkurs Künstliche Intelligenz – Eine praxisorientierteEinführung, ISBN: 978-3-658-13548-5, Springer (2016), 396 p.

V. Wittpahl, Künstliche Intelligenz: Technologie, Anwendung,Gesellschaft, ISBN: 978-3-662-58041-7, Springer (2019), 286 p.

P. Buxmann, H. Schmidt, Künstliche Intelligenz – Mit Algorithmenzum wirtschaftlichen Erfolg, ISBN: 978-3-662-57567-3, Springer(2019), 208 p.



Other Literature [2]

R. Gläß, Künstliche Intelligenz im Handel 2 – Effizienz erhöhenund Kunden gewinnen, ISBN: 978-3-658-23925-1, 56 p.

P. Gentsch, Künstliche Intelligenz für Sales, Marketing undService – Mit AI und Bots zu einem Algorithmic Business -Konzepte und Best Practices, ISBN: 978,3-658-25376-9, 295 p.

R. Huss, Künstliche Intelligenz, Robotik und Big Data in derMedizin, ISBN: 978-3-662-58150-6, 116 p.

M. Bramer, Logic Programming with Prolog, ISBN:978-1-4471-5486-0, Springer (2013), 253 p.

D. Merrit, Building Expert Systems in Prolog, ISBN:978-1-4613-8913-2, Springer (1989), 358 p.

D. v. Dalen, Logic and Structure, ISBN: 978-1-4471-4557-8,Springer (2004), 263 p.



Basics Artificial Intelligence





Goals of this chapter [1]

After studying this section and performing the practical tasks included,you should have acquired the following knowledge:

• Knowledge of the definitions of the term Artificial Intelligence,including its phases and history.

• Knowledge of the methods and applications of ArtificialIntelligence to distinguish different directions of the area.

• Knowledge about agents (software agents vs. hardware agents)and knowledge-based systems.

• Knowledge about the basic technologies of Artificial Intelligence.




Introduction to Artificial Intelligence

Basics Artificial IntelligenceIntroduction to Artificial Intelligence





Discussion: Introduction to Artificial Intelligence [1]

Open discussion about the following questions:

• Who knows the definition of the term Artificial Intelligence?

• How would you define Artificial Intelligence on your own?

• Do you think it is simple to define the term Artificial Intelligence?

• Do you know the self-named father of Artificial Intelligence?

• Why could it be difficult to define Artificial Intelligence?





Definition(s) Artificial Intelligence [1]

The term Artificial Intelligence (shortened: AI) first appeared in1956 [McCarthy et al., 1955]. The computer scientist John McCarthychooses the English term Artificial Intelligence as heading in a projectproposal and defined the term as follows:

It is the science and engineering of making intelligent ma-chines, especially intelligent computer programs. It is relatedto the similar task of using computers to understand humanintelligence, but AI does not have to confine itself to methodsthat are biologically observable.






Translated into other words, this means that the goal of AI is todevelop machines that behave as if they had intelligence.

There are another definitions for the term artificial intelligence, ofcourse. The computer scientist Elaine Rich defines the term AI asfollows [Rich and Knight, 1991]:

Artificial Intelligence is the study of how to make computersdo things at which, at the moment, people are better.






The Encyclopædia Britannica defines the term AI as follows1:

Artificial Intelligence is the ability of a digital computer orcomputer-controlled robot to perform tasks commonly asso-ciated with intelligent beings.

The computer scientist George Luger defines the term AI as follows[Luger, 2008]:

Artificial intelligence (AI) can be defined as the branch ofcomputer science concerned with the automation of intelli-gent behavior.






However, an official and final definition of the term AI does still notexist. It is also not foreseeable when the term can be defined. Rather,a definition is not even possible at the moment. This is because of theincluded phrase:

• It exists a common definition for the first phrase, artificial: madeby people, often as a copy of something natural.

• It does not exist a common definition for the second phrase,intelligence. This means that no one can clearly say what isintelligence and what makes something intelligent.

1https://www.britannica.com/technology/artificial-intelligence


https://www.britannica.com/technology/artificial-intelligence



History Artificial Intelligence

Basics Artificial IntelligenceHistory Artificial Intelligence





Discussion: History Artificial Intelligence [1]


• Do you know why Apple has an apple as symbol?

• Do you believe that machines are capable to think?

• Do you use the computer program Eliza in your private orprofessional life?

• Which programming languages in the area of ArtificialIntelligence do you know?

• How long do you think Artificial Intelligence exists?





Important scientiests/influencers [1]

According to the literature, the area of AI emerged in the 1940s[Buchanan, 2005]2. Four of the people who have significantlyinfluenced the knowledge of AI are:

• Alan Turing (logician, mathematician, cryptanalyst, computerscientist) develops the so-called Turing test.

• Joseph Weienbaum (computer scientist and computer critic)develops the chatbot Eliza.

• John McCarthy (named: father of AI) is the first to use the termAI and developed the programming language LISP.

• Alain Colmerauer (computer scientist) invents the programminglanguage Prolog.

2https://aaai.org


https://aaai.org




Important historical events [1]

(Selected) important historical events that influenced the area of AI[Buchanan, 2005]:

1943 Warren McCulloch and Walter Pitts model neural networks.

1950 Alan Turing develops the Turing test.

1956 John McCarthy is the first to use the term AI.

1958 John McCarthy invents the programming language LISP.

1966 Computer scientist Joesph Weizenbaum develops ELIZA, thefirst chatbot.





Important historical events [2]

1972 Alain Colmerauer invents the declarative (logic) programminglanguage Prolog.

1972 F. T. de Dombal develops an expert system for the diagnosis ofabdominal diseases.

1990 Pearl, et. al. use Bayes nets for AI.

2009 Google presents its first autonomous vehicle.

2011 The software Watson (manufacturer: IBM) presents two humanJeopardy masters.

2015 The vehicle manufacturer Daimler presents its firstautonomous truck.





The Turing-Test (Alan Turing, 1950) [1]

In 1950, Alan Turing published the article Computing Machineryand Intelligence [Turing, 1950]. In this article, he examined thequestion of whether a machine can think:

“Can machines think?”;

He used an imitation game to answer questions from a human (asecretary). This game is now called the Turing-Test, and one of themain milestones in the history of AI (see Figure 1).






Figure 1: Illustration of the Turing Test (source: www.pc-magazin.com).






The Turing-Test performs as follows:

• A questioner uses a keyboard and a screen to ask questions totwo interlocutors. One interlocutor is a person, the otherinterlocutor is a machine.

• After at least five minutes of questioning, the questioner mustdecide which of the two interlocutors was the person and whichof the two interlocutors was the machine.

• If the questioner cannot distinguish between man and machineafter the interview, the test is passed.






The Chatbot Eugene Goostman was the first machine to pass thetest in 2014 [The Guardian, 2014]. However, this claim is controversialand not sure.

In general, some scientists doubt the usefulness of the test. There areseveral understandable reasons for this, two of them are:

• Although the test is objective, the test is (ver)-falsifiable becausethe result depends heavily on the complexity of the input.

• The Turing test therefore only considers the language, not theappearance or the motor skills.

• Also not every human would pass the test.






Because of the importance of Turing’s work, an award is named afterhim, the so-called Turing Award. The yearly award is the highestaward in the scientific discipline Computer Science, and considered atthe rank of a Nobel Prize in that area. Until now, no German awardwinner exists. In 2018, three scientists were awarded for their work onNeuronal Networks, respectively Deep Learning[Association for Computing Machinery, 2019]:

• The computer scientist Yoshua Bengio (Canada, France),

• The computer scientist Yann LeCun (France).

• The computer scientist and psychologist Geoffrey Hinton(United Kingdom).





The programming language LISP (John McCarthy, 1958) [1]

In 1958 (published: 1960), the Massachusetts Institute ofTechnology (shortened: MIT) around John McCarthy, developedthe programming language LISP (full name: List Processing)[McCarthy, 1960]. LISP, along with the programming languageProlog, is one of the most important programming languages in AI.

Currently, there are different dialects in LISP, which is why thelanguage is not called a single language but rather a language family.The recently most relevant dialect is Common LISP3. This is a dialectstandardized after the character code published and named after theAmerican National Standards Institute (shortened: ANSI)4.

3https://lisp-lang.org/4http://ascii-table.com/ansi-codes.php


https://lisp-lang.org/

http://ascii-table.com/ansi-codes.php




The chatbot ELIZA (Joseph Weizenbaum, 1966) [1]

In 1966, Joseph Weizenbaum published the article ELIZA - AComputer Program for the Study of Natural LanguageCommunication between Man and Machine [Weizenbaum, 1966].He developed the program ELIZA, which is able to communicate witha person (secretary) in natural language (see Figure 2). The followingdemo example shows why the program would not have passed theTuring Test5:

� Human: Hello Eliza, how was your weekend?

� Machine: Hello, how are you?

� Human: How can I support you?

� Machine: Probably that is not simple...?





The chatbot ELIZA (Joseph Weizenbaum, 1966) [2]

Figure 2: Eliza user interface.





The programming language Prolog (Alain Colmerauer, 1970) [1]

Since 1970, Alain Colmerauer has published various publications ontechnologies that can deal with natural language (referred as: NaturalLanguage Processing, shortened: NLP)[Colmerauer and Roussel, 1992]. In 1972, he and his colleaguesfinally published the article Un système de communication enfrançais, in which they are describing an application known today asProlog (full name: Programming in Logic) [Colmerauer et al., 12]. Thefollowing features characterize the language:

• It is a logical programming language.

• It describes a problem, not the solution to a problem.

• It uses horn clauses.





Phases in Artificial Intelligence [1]

The former director and CEO (Wolfgang Wahlster) of the GermanResearch Center for Artificial Intelligence (shortened: DFKI,German:Deutsches Forschungszentrum für KünstlicheIntelligenz)6, founded in 1988, divides the history and modern timesof AI into four phases, with the degree of intelligence of the machineincreasing with each phase (see Figure 3) [Wahlster, 2017]:

Early Enthusiasm (1956 - 1969): Development of so-called heuristicsystems, especially heuristic (approximate solution) methods forsearch and conclusion.






Knowledge-based expert systems (late 1960s - mid 1980):Implementation of knowledge-based systems, i.e. machineknowledge processing using manually created knowledge bases;

Learning Systems (from early 1990s): Development of learningsystems, i.e. machine learning via mass data;

Hybrid Cognitive Systems (from 2010): Development of cognitivesystems, i.e. combination of learning methods withknowledge-based methods;






Phase 1 Phase 2 Phase 3 Phase 4

Heuristic Systems Knowledge-Based Systems Learning Systems Cognitive Systems

until 1970 until 1990 until 2010 from 2010

low high

Level ofIntelligence

Figure 3: The four phases in AI research [Wahlster, 2017].

6https://www.dfki.de


https://www.dfki.de



Agents in Artificial Intelligence

Basics Artificial IntelligenceAgents in Artificial Intelligence





Discussion: Agents in Artificial Intelligence [1]


• What do you mean by an agent?

• What kind of metrics do you use to evaluate algorithms

• Do you know how to evaluate Artificial Intelligence techniques?

• What do you think is a knowledge-based system?

• Do you know the difference between a true positive statement, atrue negative statement, a false positive statement, and a falsenegative statement?

• Why can a framework or algorithm show good accuracy results,but is still considered as bad?





Types of Agents [1]

Core elements of the methods and applications of AI are so-calledAgents, which represent a definable unit with a defined goal and areused to achieve a goal independently. The agent communicates indifferent ways with its environment, which is why two variations ofagents are distinguished [Woolridge, 2002]:

• A Software-Agent is an agent for which a suitable output isdetermined on the basis of user input.

• A Hardeware-Agent is an agent in which a change takes placeon the basis of the environment, i.e. without user input. Ahardware agent works with sensors and a software agent isincluded to determine the appropriate output.





Types of Agents [2]

Besides the above-mentioned variants of agents, there exist alsosub-variants of agents [Woolridge, 2002]:

• A network of several software agents is a so-called Multi-Agent(system).

• If an agent system distributes its architecture, this is a so-calledCollaborative-Agent (system).





Types of Agents [3]

Software-Agent

Input

Output

User

Figure 4: Schematic Structure of a Software Agent [Ertel, 2016].





Types of Agents [4]

Hardware-Agent

Software-Agent

Sensor (1-N)

Actuator (1-N)

Cognition

Output

Surroundings

Figure 5: Schematic structure of a hardware agent [Ertel, 2016].





Types of Agents [5]

The agent either reacts only to the currently underlying information(reflex agent) or includes the past (agent with memory). Based on thedifferent types and amount of information different properties ofagents are used [Huber and Fischer, 13]:

Autonomous i.e. the agent acts independently.

Reactive i.e. the agent reacts to the environment.

Goal-Oriented i.e. the agent behaves on the basis of the goal.

Communicative i.e. the agent communicates with others. That canbe humans or other agents.

Learning i.e. the agent has the ability to learn.

Mobile i.e. the agent is used as software or robot.

Personal i.e. the agent is intended for contact with humans.





Types of Agents [6]

Some known applications of agents are [Huber and Fischer, 13]:

Medicine uses agents primarily to create 3D images.

Space Travel for independent collection of information.

Search Engines to adapt to the search habit of the user.

Computer Games to increase the joy of playing with bots.





Evaluation of Agents [1]

The standard metrics in AI to evaluate and compare differentapproaches are based on technologies to evaluate the quality ofcategorization. The standard metrics are:

• Precision (shortened: P) is the measure of correctness (seeEquation 1).

• Recall (shortened: R) is the measure of completeness (seeEquation 2).

• F-Measure (shortened: F1) is the harmonic mean of theabove-mentioned metrics (see Equation 3).






The above-mentioned metrics, as well as its related metrics, arebased on comparing correct classified artifacts against incorrectclassified artifacts. Hereby four type of classes are distinguished (seeFigure 6):

• True-Positive (shortened: TP) is an actual relevant class thatwas identified through the agent as relevant.

• True-Negative (shortened: TN) is an actual non-relevant classthat was identified through the agent as non-relevant.

• False-Positive (shortened: FP) is an actual irrelevant class thatwas identified through the agent as relevant.

• False-Negative (shortened: FN) is an actual relevant class thatwas identified through the agent as non-relevant.






TruePositive

ComputedRelevant

RelevantItems

FalsePositive

IrrelevantItems

FalseNegative

ComputedIrrelevant

TrueNegative

CorrectC

lassification

Incorrect Classification

Figure 6: Quality measures for classifiers.






P =

∑TP∑

TP +∑

FP(1)

R =

∑TP∑

FN +∑

TP(2)

F1 = 2×P ×RP + R

(3)






Besides the standard metrics, another metrics exist to measure thequality of classifiers. The following metrics are also used:

• Accuracy (shortened: A) measures systematic errors, alsocalled hit rate (see equation 4).

• Balanced Accuracy (shortened: BA) exists to measuresystematic errors of unbalanced datasets (see equation 5).

• Sensitivity (shortened: SE) is the rate of true positivestatements that are correct classified (see equation 6).

• Specificity (shortened: SP) is the rate of true true negativestatements (see equation 7).






A =

∑TP +

∑TN∑

TP +∑

FP +∑

FN +∑

TN(4)

BA =SE + SP

2(5)

SE =

∑TP∑

TP +∑

FN(6)

SP =

∑TN∑

TN +∑

FP(7)






A prominent example for evaluating agents is the assignment to aclass based on an information source (e.g. spam filter for emails).Suppose the following example [Ertel, 2016]:

• There are two agents – Agent 1 and Agent 2.

• Both agents have the task of classifying 1,000 emails.

• The agents classify whether the email is a spam message or not.

Which agent is better (see Tables 1 and 2)?






Agent 1 classifies 799 emails as spam, which are actually spammessages. Agent also classifies 189 as non-spam, which areactually not spam messages. 11 Messages are classified asspam even though they are not spam. 1 Message is classified asnon-spam even though it is actually a spam message.






Agent 2 classifies 762 as spam, which are actually spam messages.Agent also classifies 200 as non-spam, which are actually notspam messages. 0 messages are classified as spam eventhough they are not spam messages. 38 Messages are classifiedas non-spam even though they are actually spam messages.






Table 1: Exemplary classification of the results of two agents.

Measure Agent 1 Agent 2True Positive 799 762True Negative 189 200False Positive 11 0False Negative 1 38






Table 2: Example comparator of two agents for F-Measure score.

Precision 0.98 1Recall 0.99 0.95

F-Measure 0.98 0.97





Knowledge and Agents (Knowledge-Based Systems) [1]

An elementary component of an agent is knowledge, i.e. theknowledge about how the agent should react to an input orperception. In order to map this knowledge, Knowledge-BasedSystems have a so-called Knowledge Base [Ertel, 2016].

Relationships between data are mapped in the knowledge base.Based on these relationships, a conclusion can then be drawn aboutthe desired reaction of the agent.






Knowledge-based systems consist of three areas (see figure 7):

1. The knowledge acquired from knowledge sources such asdatabases or the environment and the knowledge engineeringprocess or machine learning.

2. The knowledge base that has been created by means of theknowledge acquisition outlined above on the basis of existingstatic or dynamic knowledge sources.

3. The inference mechanism, which draws a final conclusion for theproblem under consideration on the basis of the knowledge base,if possible.






The knowledge to be represented can be represented by formallanguages (propositional logic, predicate logic, probabilistic logic,fuzzy logic, decision trees). The separation of knowledge andinference has decisive advantages:

• The inference is application-independent.

• knowledge can be stored declaratively (i.e. describing theproblem).






KnowledgeResource

Database

Surroundings

KnowledgeAcquisition

KnowledgeEngineering

MachineLearning

Data

KnowledgeBase

KnowledgeProcessing

Inference

User

Question

Answer

Figure 7: Structure of a knowledge processing system [Ertel, 2016].




Areas of Artificial Intelligence

Basics Artificial IntelligenceAreas of Artificial Intelligence





Discussion: Areas of Artificial Intelligence [1]


• How would you categorize the areas of Artificial Intelligence?

• Which disciplines apart from Computer Science do you thinkinfluence the (research) areas of Artificial Intelligence?

• What do you think is the difference and overlap between DeepLearning and Neuronal Networks?

• Which methods in Artificial Intelligence do you know?

• Which applications in Artificial Intelligence do you know?

• Why is classification important in terms of Artificial Intelligence?

• What could be the difference between Test and Training Data?

• Do you know what a Data Scientist is doing?

• Do you know or use Data Mining technologies?





Strong vs. Weak (Narrow) Artificial Intelligence [1]

In general, two kind of areas in AI are distinguished[Paschek et al., 2017]:

• Strong Artificial Intelligence] is when the machine has thesame intellectual abilities as a human being. Recently, notechniques that can be classified as Strong AI exist.

• Weak Artificial Intelligence is when the machine solves aproblem based on provided methods. In this case, the machine isonly capable to solve the problems defined, often a singleproblem. Exemplary Applications for Weak AI are imageprocessing, (natural) language processing, expert systems, etc.





Subdivising Artificial Intelligence [1]

There are different ways and models to subdivide the subject areaArtificial Intelligence, which is a branch of computer science. Asubdivision is therefore complex, as the subject area of AI isinfluenced by many other sciences:

Psychology deals with intelligence. This science influences the AI,because it defines what, when and why something is intelligent.

Logic deals with methods of reasoning. Thus, this science influencesthe AI because it is an essential technology in the AI.

Computer Science represents the superordinate discipline andinfluences the AI through advances in data structures,algorithms, programming languages.






Cognitive Science investigates processes and the experience ofprocesses. It thereby influences how something can beevaluated.

Pedagogy deals with education and upbringing. This is particularlyimportant for learning processes and the extent to which someknowledge can be acquired.

Linguistics deals with language. With regard to AI this means inparticular the handling of language as well as the semantics oflanguage.

Physiology deals with the body. This influences AI technologiesused in medicine.






One possibility to subdivide the subject area AI is to divide it into twofacets – namely methods vs. applications, as done in the GablerWirtschaftslexikon7 (see Figure 8).

Methods focus on what underlying technology (or technologies) areused so that the approach is capable for a problem.

Applications focus on how respectively why the technology (ortechnologies) is used to solve a specific problem.






The most important AI methods are:

• Neuronal Networks are networks of artificial neurons. Thetechnology Deep Learning is used to apply neuronal networks incomputer science. This area has recently gained enormousimportance. Deep learning is one kind of Machine Learning,whereby not all Machine Learning approaches are neuronalnetworks.

• Knowledge Representation serves the formal representation ofknowledge. This representation is often done through thegraph-based (hierarchically structured) methods – namelyontologies, taxonomies, or through non-hierarchically structuredfolksonomies. Such information is used inside Expert Systemsto deduce new knowledge.






• Logical Reasoning involves classifying something on the basisof statements and their logic. The underlying methods used arederived from Propositional Logic and First-Order Logic. Themain programming language focussing on logical reasoning isProlog.

• User Models refers to topics related to accessibility andpersonalization of systems. Hereby, the focus is on the interactionbetween human and computer. Important applications using thismethods are Expert Systems acting as RecommenderSystems, Decision Support Systems, and Adaptive Systems.

• Case-Based Methods refer to a procedure in which newproblems are solved on the basis of problems already solved.Hereby, the system learns from the results of previousclassifications.






The most important AI applications are:

• Speech Processing includes applications around analysis andinteraction using speech. E.g., this is used in Signal Processing,and Natural Language Processing (shortened: NLP).

• Intelligent Database Systems store knowledge about entities.Caution, this term is very freely interpretable.

• Knowledge Engineering serves to map knowledge. This is usedin Expert Systems and for Data Mining.

• Expert Systems serve to use knowledge to acquire newknowledge or to make decisions. E.g., in RecommenderSystems, and Decision Support Systems.

• Robotics serves to create technical applications to solveeveryday tasks or unreasonable tasks.







Applications

RoboticsExpertSystems

IntelligentDatabaseSystems

SpeechProcessing

Methods

Case-BasedMethods

UserModels

LogicalReasoning

KnowledgeRepresentation

NeuronalNetworks

Figure 8: Methods and Applications in the area of Artificial Intelligence

7https://wirtschaftslexikon.gabler.de/definition/kuenstliche-intelligenz-ki-40285


https://wirtschaftslexikon.gabler.de/definition/kuenstliche-intelligenz-ki-40285

https://wirtschaftslexikon.gabler.de/definition/kuenstliche-intelligenz-ki-40285




Machine Learning [1]

Machine Learning (shortened: ML) means that an agent is able tolearn solutions for problems through a growing set of training datarepresenting the problem spectrum and to apply this knowledgeindependently to the effect of multiple characteristics of a specificproblem to be addressed.

The task of the agents is to perform classifications of a problem.Hereby, the existing characteristics are used to determine whether acategory applies, i.e. is true, or does not apply, i.e. is false.

For example, it is determined whether a customer has a preference fora product or not, i.e. a linear classification is carried out.






The categorization process would be as follows:

1. The agent is provided with data about the customer (e.g.personal data, order history, etc.).

2. The agent is provided with data about the product (e.g. productgroup, attributes, price, etc.).

3. The agent uses the training data to determine whether a possiblepreference exists or not.

4. The agent determines whether a preference exists or not, andvalidates the result if necessary.






Of course, there exist different algorithms to perform theclassifications. The four most important directions are [Ertel, 2016]:

• Linear Classification takes place by means of a linearlyseparable two-dimensional set of data (see Figure 9).

• Nearest Neighbour (shortened: k-NN) is performing aclassification based on the nearest neighbor to a variable (seeFigure 10).

• Naive Bayesian Classifier is performing a classification basedon the probability of what is already known (see Figure 11).

• Clustering has the aim to group variables based on theirsimilarity (see Figure 12).






Figure 9: Linear Classification.






Figure 10: Nearest Neighbour Classification.






Figure 11: Naive Bayes Classifier.






Figure 12: Clustering.






In order for an agent to make a statement as precisely as possible,certain components are necessary:

• Test Data, which indicates whether the agent can generalizeprecise knowledge.

• Training Data that contains the knowledge to solve problems.

• Performance Metrics, which say how precisely the agent acts.





Data Mining [1]

Data Mining means that knowledge is derived on the basis of existingand mostly heterogeneous data and prepared for humaninterpretation so that decisions can be made.

It is considered as the process of gaining knowledge from data as wellas its representation and application is called data mining. A crucialrole play statistical measures in data mining e.g.:

• Mean Values, especially the harmonic mean, as well as thearithmetic mean.

• Standard Deviation to compute the variation of data.

• Covariance also to measure the variation of data, i.e. betweentwo variables.

• Correlation Coefficient to measure the relationship betweentwo variables.





Data Mining [2]

The task of data mining is to provide a basis for decision-making,often referred as Data Science. Meanwhile, there are various tools tofacilitate the data mining process. The tools usually provide acomfortable graphical user interface to visualize data as well astechnologies to prepare the data. Important tools in the data miningenvironment are:

• RapidMiner8 is most prominent tool in the area of data mining.

• Clementine9 based on the statistical programm SPSS, which isprovided by the International Business Machines Corporation(shortened: IBM).





Data Mining [3]

DEMO: RapidMiner

1. Download RapidMiner10.

2. Go to My RapidMiner11.

3. Log-In and navigate to Academy.





Data Mining [4]

Meanwhile, there is a separate professional group that deals with datamining, called Data Scientists. Data scientists usually act as a linkbetween the Business Intelligence (shortened: BI) department andother specialist departments (e.g. marketing, production). Their coretask is to advise specialist departments and decision-makers.

Such professionals often have a mixture of a technical background(programming languages, databases, etc.) to control and adapt thedata mining processes and a business background (statistics).





Data Mining [5]

A good example for data mining is grouping. For example, when anagent is used to determine if two customer groups are similar to carryout marketing activities. The process would be as follows:

1. The agent is provided with data about the customer groups (e.g.name, sub-customer groups, etc.).

2. The agent is provided with data on the purchasing behavior of thecustomer groups (e.g., preferences, price ranges, etc.).

3. The agent is provided with data on marketing measures alreadytaken (e.g. acceptance of measures, effect of measures, etc.).

4. The similarity measures are finally combined, and the agentprovides various diagrams to underline the similarity.





Data Mining [6]

For mining data, several data sources are usually often used, oftennamed as Data Warehouse (see Figure 13). The data inside such awarehouse may differ with regard to different criteria. This problem isreferred as (information) Heterogeneity. Heterogeneity in the contextof data is the cognitive and methodological difference between twotypes of information. There are four types of heterogeneity of data:

• Terminological Heterogeneity means that two pieces ofinformation differ based on your language - either because thereare two different languages, because there are differentsub-languages (e.g. dialect), or because synonyms are used.

• Conceptual Heterogeneity means when the semantics of theinformation differ. Different axioms are then used to map theinformation and the statements that are mapped differ.





Data Mining [7]

• Syntactic Heterogeneity means that different data languagesand models are used. For example, if one information is mappedwithin a hierarchical database and the other information ismapped in a relational database.

• Semiotic Heterogeneity exists when the information isunderstood or interpreted in different ways. For example, if oneperson regards a large passenger car as a bus and the other asa station wagon.





Data Mining [8]

Figure 13: Illustration of a Data Warehouse (source: www.dremio.com).

8https://rapidminer.com/9?https://www.the-data-mine.com/Software/ClementineSoftware

10https://rapidminer.com/11https://my.rapidminer.com


https://rapidminer.com/

?https://www.the-data-mine.com/Software/ClementineSoftware

https://rapidminer.com/

https://my.rapidminer.com




Neural Networks [1]

Artificial Neuronal Networks (shortened: ANN), often referred justas Neuronal Networks, are a sub-area of Machine Learning(shortened: ML). ANN are inspired from the structure and the workingof a human brain [JUST ADD AI GmbH, 2019, Luber and Litzel, 19].

The application of ANN is referred as Deep Learning (shortened:DL), wherefore both terms (ANN and DL) are often used synonymical.DL has gained high research interest during last years.

ANN are very similar to ML approaches. The main difference is thedegree of automatization. In simple words, ML approaches require anexpert to define the best features (algorithms, data, threshold, etc.) fora specific problem, whereas ANN find the best solution on its own(see Figures 14 and 15).





Neural Networks [2]

Input Feuature Extraction Classifier OutputHuman Machine

Figure 14: The main processes of Machine Learning appraoches.





Neural Networks [3]

Input Feuature Extraction + Classification OutputMachine

Figure 15: The main processes of Artificial Neural Network appraoches.





Neural Networks [4]

An ANN consists of layers that include multiple of so-called neuronsthat are connected with the neutrons of the following layer. Minimal,an ANN consists of three layers: (see Figure ??)[Miikkulainen et al., 2019]:

• Input Layer is responsible for taking in the information in theform of patterns or signals.

• Hidden Layer is responsible for generating the output based onthe input information.

• Output Layer is responsible for putting out the result.





Neural Networks [5]

Input #1

Input #2

Input #3

Input #4

Output

Hiddenlayer

Inputlayer

Outputlayer

Figure 16: The structure of a three-layered Artificial Neuronal Network.





Neural Networks [6]

Each neuron of the different layers is acting as a single unit to processinformation, including computation and in/output. For that reason, theANN has often more than three layers. Then, multiply Hidden Layersexist. This has benefits and drawbacks:

• With adding layers, and through this, adding neurons, thecomputation accuracy usually increases – the approach achievesbetter results.

• However, with adding layers, and through this, adding more stepsof computation, the computation performance usually decreases– the approach performs more slowly.





Neural Networks [7]

Depending on how many layers are used in total, the ANN is calledaccordingly. In detail, the number of layers is added to the term ANN,in the form of:

k −ANN, (8)

whereby k stands for the number of layers. For example:

• A minimal ANN having three layers is called a three-layered ANN(see Figure 16).

• An ANN with four layers is called a four-layered ANN (see Figure17).

• An ANN with five layers is called a five-layered ANN (see Figure18), and so on.





Neural Networks [8]

Input #1

Input #2

Input #3

Input #4

Output

Hiddenlayer 1

Hiddenlayer 2

Outputlayer

Inputlayer

Figure 17: The structure of a four-layered Artificial Neuronal Network.





Neural Networks [9]

Input #1

Input #2

Input #3

Input #4

Output

HiddenLayer 1

HiddenLayer 2

HiddenLayer 3

Inputlayer

Outputlayer

Figure 18: The structure of a five-layered Artificial Neuronal Network.





Knowledge Representation [1]

Knowledge Representation is the way of representing data in amachine-readable way. It allows the system to then gain newknowledge through Knowledge Engineering applications and tomake rule-based decisions.

Important attempts for knowledge representation are:

• Linked Data is the paradigm of structuring and publishing datafor the Semantic Web inside a semantic net, e.g. the serviceFreebase publishes databases for describing diverse artifacts-

• Directories are informally structured indexes, e.g. the InternetRetailing12 directory, or the Yahoo!13 directory are informal webdirectories.

• Annotations are any types of resources having furtherdescriptions.





Knowledge Representation [2]

• Lexicons are used to translate between languages. The mostwidely-used lexicons, also named translators, are the MicrosoftBing14 translator, or the Google15 translator.

• Thesauri are utilized to analyze semantic similarity betweendata. The biggest thesauri existing recently is the lexicaldatabase WordNet16 [Miller, 1995]. In WordNet, a synset, i.e. setof synonyms, is presented for every word sense with one or moredifferent word(s), and a class type for eachword, which can be anadjective, adverb, noun, or verb (see Figure ??). Wordnet is usedin many research contributions for knowledge reasoning.

12http://internetretailing.net/13https://local.yahoo.com/14http://www.bing.com/translator/15https://translate.google.com16https://wordnet.princeton.edu/


http://internetretailing.net/

https://local.yahoo.com/

http://www.bing.com/translator/

https://translate.google.com

https://wordnet.princeton.edu/




Logical Reasoning [1]

Logical Reasoning is the process of systematically derivingconclusions through applying series of mathematical procedures inthe form of pre-defined statements [Ertel, 2016]. Two types of logicalreasoning are distinguished:

• Deductive Reasoning gives accurate evidence, if something istrue or not true (false). For example, if you have a true statementtelling that “the street is wet” and another statement defining that“if it rains, the street is wet”. The conclusion “it rains” is true.Consequently if you would have the statement “the street is dry”instead, or you just state that the statement “the street is wet” is afalse statement, the conclusion “it rains” is false.






• Inductive Reasoning gives not an accurate evidence, but ageneralization that something might be probable true or might benot true (false). For example, if you have a statement telling that“rubber boots keep your feet dry” and another statement definingthat “person A is wearing rubber boots”, the conclusion could bethat “the street is wet, as it is raining”. This includes theprobability that “person A is wearing boots” as the street is wet,so it must have rained. Of course, the person could also havebeen wearing rubber boats when the street is dry.






As can be seen from above, the reasoning is based on statements.Such statements are mathematical procedures, which are based onPropositional Logic and its extension, the First Order Logic.

The Propositional Logic deals with the linking of propositions(statements). A proposition can be either true or not true (false),which expressed by a Truth Value. The propositional logic has theaim to transfer normal-language statements into mathematicalprocedures, for example:

� “If it rains, the street is wet”

� wet street → it rains






The connection of the statements is done by using LogicalOperators, also named Logical Connectives. Five operators plusanother connector exist (see Table 3):

• Negation means the negation of a statement.

• Conjunction means the combination of two statements bymeans of the logical and.

• Disjunction means the combination of two statements by meansof the logical or.

• Implication means a combination by if-then.

• Equivalence means the equality of two statements.

• (Absurdum) means the logical untruth of a statement.






Based on above, the syntax of propositional logic is as follows[Dalen, 2013]:

≫ Let Op = (¬,∧,∨,⇒,⇔,⊥) be the set of logical operators and∑

the set of symbols Op∩∑∩(true, false) = ∅.

≫ Let the language of the propositional logic be an alphabetconsisting of Sentence Symbols SS, Conncectors C, andHelping Symbols HS, with:� SS is a set of ss = {s0, s1, s2, . . . };� C is a set of c = {¬, ∧, ∨,⇒,⇔, ⊥};� HS is a set of hs = {(, )};






Table 3: Overview of logical operators.

Symbol Meaning Description Example¬ not Negation ¬A∧ and Conjunction A ∧B∨ or Disjunction A ∨B⇒ if . . . , than . . . Implication A ⇒ B⇔ if . . . , and only than . . . Equivalence A ⇔ B⊥ untrue Falsum, Absurdum A⊥B






The Sentence Symbols SS and ⊥ stand for non-combinablesentences, which are called atoms or atomic sentences. The set ss ofstatements in SS is the smallest set X with its properties:

≫ pi ∈ X(i ∈ N),⊥ ∈ X






Assuming there exist two statement variables A and B (∑

= (A ,B)),which can be true or false (

∑→ (true, false)), we get a truth table

(see Table 4).

Table 4: Truth Table for two statement variables (∑

= (A ,B)).

A B (A) ¬A A ∧ B A ∨ B A⇒ B A⇔ B

w w w f w w w ww f w f f w f ff w f w f w w ff f f w f f w w





User Models [1]

User Models are systems to manage the accessibility and thepersonalization of systems.

Hereby, the focus is on the interaction between two objects, namelybetween a Human and a Computer.

Human ⇐⇒ Computer





User Models [2]

Important applications using this methods are Expert Systems. Suchsystems have the aim to process something (i.e. a task, a problem) inthe same quality as a human expert would process it. Two main typesof expert systems received high research interest:

• Recommender Systems are aimed to process (semi- orsemiautomatic) and finally automatically operaterecommendations based on preferences. For example, ine-commerce to provide personalized product suggestions basedon the customers‘ purchasing history.

• Decision Support Systems are aimed to semi-automatically orautomatically provide suggestions for experts. For example, incustomer relationship management to sent newsletters toprospective customers.





User Models [3]

The underlying methods to be used in recommender systems (canalso be used for decision support systems) can be distinguished inthree major directions, whereby each direction has its own benefitsand drawbacks [Liu et al., 2013, Pin-Yu et al., 2010]:

• Collaborative Filtering is utilizing the knowledge about manyusers. For example in e-commerce the knowledge aboutcustomers being categorized into the same user-group.

• Content-Based is utilizing the knowledge about the information.For example in e-commerce the knowledge about the similarity oftwo products based on their descriptions.

• Hybrid Recommendation as the name suggests, combinesboth previous mentioned recommendation strategies to improveperformance and to overcome their limitations [Li et al., 2012].





User Models [4]

Collaborative Filtering assumes that each customer belongs to alarger group of users with similar behavior [Resnick et al., 1994]). Twoclasses of algorithms can be distinguished in this field[Liang et al., 2010]:

• Memory-Based Collaborative Filtering stands for the initiallyaim of collaborative filtering where two kind of strategies can bedistinguished, namely user-based algorithms on the one side,and item-based algorithms on the other side [Ding and Li, 2005].

• Model-Based Collaborative Filtering algorithms aim in definingpredictions. Two types of model-based algorithms represent thecore areas, namely aspect model approaches, and personalitydiagnosis model approaches.





User Models [5]

The sub-classes of Memory-Based Collaborative Filtering are:

• User-Based Collaborative Filtering approaches represent theuser as a vector in a space of items [Resnick et al., 1994].

• Item-Based Collaborative Filtering approaches represent theitems in the user space [Sarwar et al., 2001]. It hereby aims infinding the nearest neighbour inside the vector of items (user).

The sub-classes of Model-Based Collaborative Filtering areDing:2005:TWC:1099554.1099689:

• Aspect Model-Based Collaborative Filtering approaches arebased on the mixture to model users ordering behavior.

• Personality Diagnosis Model-Based Collaborative Filteringapproaches are based on a training set.





User Models [6]

The reason for such a diversity of algorithms is that in User Models,two kind of specific problems have to be considered, those are:

• Overspecialization Problem means that the approach will showhigh accuracy for a specific problem, but low accuracy for similarproblems (no cross-domain independence).

• Sparsity Problem means that the approach requires theexistence of a sufficient amount of information, often also referredas Cold-Start Problem.





User Models [7]

Content-Based Recommendations suggest similar items to thatones the user preferred in the past [Pin-Yu et al., 2010].

It measures similarities between items based on textual information,e.g. product descriptions [Vargiu et al., 2013]. This recommendationmethod follows a two-step consisting process:

1. Indexing of Products.

2. Indexing of Users





User Models [8]

Indexing of Products (also referred as: Profile Learning) is basedon implicit feedback, e.g. purchases, and explicit feedback, e.g.product reviews. Resulting is a set of Keywords associated with theitem, respectively the users profile [Werner et al., 2014]. Secondly, thecomparison between the products and the users, to recommend itemssimilar to those a given user has liked in the past [Lops et al., 2011].





User Models [9]

One special kind of content-based recommendation is the method ofKnowledge-Based Recommendation. This technique adds implicitknowledge about the user, e.g. demographic information, to deduceappropriate recommendations [Karypis, 2001].

Content-based recommendation has been proven for applications,which concentrate on the recommendation of documents relevant to atopic [Li et al., 2012]. This approaches lack of the semanticunderstanding of users preference, and so the resultedrecommendations only include items very similar to those the useralready knows [Pin-Yu et al., 2010].




Ontologies and Taxonomies

Basics Artificial IntelligenceOntologies and Taxonomies





Discussion: Ontologies and Taxonomies [1]


• Do you know how two datasets can differ?

• What concepts do you know to organize data/information?

• Why do you think it is important to organize data/information?

• Which benefits can arise when organizing data?

• Do you know any languages to structure data?

• Do you know which kind of systems are responsible that anonline-shop knows what you want?

• How would you classify different academic roles?





Semantically Structuring Data [1]

Knowledge Representation is based on techniques defining how thedata is represented. Two main methods exist[Angermann et al., 2017]:

• Ontologies are used to represent a domain of interest in astructured and semantic way. Through this, relationshipsbetween single leaves can be derived. An ontology is able todescribe relationships in the form of is-a relationships, but alsoarbitrary complex relationships.

• Taxonomies (also named directories or e-catalogs) aresubcategories of ontologies. Those are describing a domain ofobjects with similar properties inside a semantically structuredout-tree, e.g. “A car is-a vehicle”. Contrary to ontologies, ataxonomy is only describing is-a relationships, but not arbitrarycomplex relationships like ontologies.






All kinds of (semantic) relationships that can be expressed by anontology, and in parts by a taxonomy are the following (see Table 5):

• Hypernym relationships express that something superordinatessomething, e.g. “Vehicle” superordinates “Cars”.

• Hyponym relationships express that something subordinatessomething, e.g. “Cars” subordinate “Vehicles”.

• Meronym relationships express that something is part-ofsomething, e.g. “Wheels” part-of “Cars”.

• Antonym relationships express that something is the opposite-ofsomething, e.g. “great” opposite of “bad”.

• Synonym relationships express that something is identical-toanother thing, e.g. “great” identical to “amazing”.






Table 5: Overview of Semantic Relationships.

Symbol Meaning Description Example⊃ superordinate Hypernym A ⊃ B⊂ subordinate Hyponym B ⊂ A∈ part-of Meronym C ∈ B, opposite-of Antonym D , E= indentical-to Synonym D = F





Formalism and Syntax [1]

Formally, an ontology is using a set of Concepts, as well as a set ofEdges, with (see Equation 9) [Angermann and Ramzan, 2016]:

Θ = ({Φ}, {Λ}), (9)

which is using a set of concepts Φ for describing terms with a Label,i.e. name of the concept, and a set of edges Λ connecting lessgeneral with more general concepts of different levels. The edgesbetween the concepts represent the hierarchical relationships insidethe taxonomy. For example, an ontology (or taxonomy) consisting ofthree hierarchically ordered levels utilizes a root concept as the mostgeneral concept, different super concepts detailing a root concept,and sub concepts detailing the super concept, which is in turn, a subconcept of the root concept (see Figures 19 and 20).






A

JIB

HGFEDC

Figure 19: Hierarchical structure of an exemplary Taxonomy/Ontology.






Root Concept

Super Concept

Sub ConceptSub Concept

Super Concept

Sub ConceptSub Concept

Figure 20: Different concept types of a Taxonomy.






A single concept φC is a Sub Concept (Hyponym), formally subof , if itis a less generalized concept of another concept, φB , as given inEquation (10), if:

φC = subof(φB) :⇔ (φC ⊂ φB)∧ ((φC ∧φB) ∈ Φ), (10)

where φC and φB are two concepts of taxonomy Θ described throughΦ and Λ. This relationship is also referred to as is-a relationship.Consequently, a Super Concept φB , formally superof , is a moregeneralized concept of φC , as given in Equation (11), if:

φB = superof(φC) :⇔ φC = subof(φB). (11)






A Sibling Concept φD of φC , formally sibof , is the relationshipbetween two concepts sharing the same super concept, as given inEquation (12), if:

φD = sibof(φC) :⇔ (φD ∧φC) = subof(φB). (12)

A Root Concept φA , formally rootof , is a concept that has no superconcept, as given in Equation (13), in which:

A = rootof(Θ) :⇔ @superof(φA ). (13)






The labels of concepts often consist of a combination of different wordsequences, i.e. Multi-Word Expressions (shortened: MWE). MWEare defined as idiosyncratic interpretations that cross word boundaries(or spaces) [Sag et al., 2002].

Besides the label, each concept can have an optional description (e.g.“A . . . used for . . . ”), and a set of optional properties acting asadditional metadata (e.g. Color).

The creation of the taxonomy is either performed through expert(s)knowing the technical details of the entities belonging to a concept, orby matching to formal resources, e.g. to a standard taxonomy, whichprovides pre-defined sets of concepts for specific domains. Toconstruct taxonomies, the authors in [Kang et al., 2014] defined threetasks:






1. Building the taxonomy, either through a bottom-up approach, i.e.combination of sub concepts, or with a top-down approach, i.e.splitting of super concepts.

2. Grouping the concepts, which is predominantly achieved byreferring to background knowledge resource(s). For example to astandard taxonomy, or the semantic lexicon WordNet, which isthe most widely used resource [Fellbaum, 1998].

3. Assigning the super concepts, meaning to determine theabove-mentioned is-a relationships by grouping sub concepts.





Ontology/Taxonomy in Information Management [1]

During the last decade, taxonomies, as well as ontologies have arisento be an essential part in various information management systemsand business applications, like in [Phan and Vogel, 2010]:

• Product Information Management (shortened: PIM) forefficiently managing the product categories.

• Customer Relationship Management (shortened: CRM) forefficiently managing the customer groups.

• Product Lifecycle Management (shortened: PLM) for efficientlymanaging product relationships.

• Enterprise Resource Planning (shortened: ERP) for efficientlymanaging business processes.

• Electronic Commerce (shortened: E-Commerce) for allowingan efficient product navigation process in multilingualOmni-Channel environment (see Figures 21, 22, and 23).






Shop by Department

Car & Motorbike

Motorbike Accessories & PartsTools & EquipmentCar Accessoires & Parts

Figure 21: Amazon product taxonomy (English, extract).






Alle Kategorien

Auto & Motorrad

MotorradWerkzeuge & WartungAutoteile & Zubehoer

Figure 22: Walmart product taxonomy (German, extract).






All Departments

Department: Auto & Tires

Auto Detailing & Car Care

Pressure WorkersCleaning ToolsCar Wash & PolishCar Washers & Cleaner

Figure 23: Walmart product taxonomy (English, extract).





Storing Semantic Knowledge [1]

For storing and managing the ontologies, and taxonomies, differenttechnologies exist. The most important are:

OWL stands for The Web Ontology Langauge17. It is a languageusing Extensible Markup Language (shortened: XML), aprogramming language for managing data stored inside ahierarchical database system describing entities with the help ofmarkups.

RDF stands for Resource Description Framework18, and isanother XML-based language and a subset of OWL.





Storing Semantic Knowledge [2]

SPARQL stands for SPARQL Protocol And RDF QueryLanguage19, and is the mainly used language to query againstthe taxonomies. It is also based on XML.

Protégé is an editor for constructing and managing ontologies, aswell as taxonmies20. The editor is supporting theabove-mentioned languages.

17https://www.w3.org/OWL/18https://www.w3.org/2001/sw/wiki/RDF19https://www.w3.org/2001/sw/wiki/SPARQL20https://protege.stanford.edu/d


https://www.w3.org/OWL/

https://www.w3.org/2001/sw/wiki/RDF

https://www.w3.org/2001/sw/wiki/SPARQL

https://protege.stanford.edu/d




Heterogeneity of Data [1]

When interchanging data based on ontology, taxonomy, or in parts,folksonomy, the quality result performed through a matching operationis significantly affected by the the cognitive and methodical disparitybetween two taxonomies, named Taxonomic Heterogeneity.

For example, when the marketing expert of Walmart, as given inFigure 23 as source taxonomy ΘA , wants to order price-lists from aGerman supplier producing sparkling plugs, as given in Figure 22 astarget taxonomy ΘB . Four categories of heterogeneity exist to detailthe kind of differences between taxonomies[Euzenat and Shvaiko, 2013]:






• Terminological Heterogeneity appears when the labels ofconcepts, are different [Euzenat and Shvaiko, 2007]. Eitherbecause of different languages, e.g., ΘA in English, and ΘB inGerman, when using different technical sublanguages, or whenusing synonyms.

• Semiotic Heterogeneity emerges when persons misinterpretconcepts, respectively the is-a relationships between theconcepts. For example, a customer does not expect “PressureWorkers” to detail “Auto Detailing & Car Care”, as this tool canalso be used for cleaning houses and gardens.






• Conceptual Heterogeneity arises if two taxonomies are usingdifferent models [Euzenat and Shvaiko, 2013]. The taxonomiesare representing the domain with different axioms, i.e. truestatements, or different concepts [Euzenat and Shvaiko, 2007].For example, the concept “Auto Detailing & Car Care” has foursub concepts, whereas the concept “Auto & Motorrad” has onlyfour sub concepts, but some of the concepts are semanticallysimilar.






• Syntactical Heterogeneity occurs when different datalanguages/models are used to store the taxonomies[Euzenat and Shvaiko, 2007]. For example, ΘA is stored in OWL,but ΘB is stored in RDF. As OWL has a precise mapping to RDF,thus is comparable with arbitrary RDF graphs. If languages areusing a different knowledge representation formalism, matchingapproaches are required to translate between the schemata.




Some Algorithms & Techniques for AI

Basics Artificial IntelligenceSome Algorithms & Techniques for AI





Discussion: Some Algorithms & Techniques for AI [1]


• Can you name some algorithms that are used in ArtificialIntelligence?

• How would you categorize different algorithms used in ArtificialIntelligence?

• How would you compute the difference between two wordphrases?





Algorithm Categorization [1]

The algorithms and techniques used for AI differ according to themethod used, and the application desired. For that reason, differentkinds of how to categorize the algorithms exist. However, theunderlying core algorithms and techniques, including additionalresources like databases are often the same.

On the following slides, different techniques (including algorithms) arepresented that are used for different kinds of AI applications. Ascategorization for the algorithms, the categorization as used in theresearch area Taxonomy Matching is utilized. In this area, two maindirections of techniques are distinguished:

• Element-Level Techniques using literal values, and/or itsproperties, for measuring semantic similarity.

• Structure-Level Techniques are using the formal structureinside the taxonomy to compute similarity between concepts.





Formal-Resource-Based [1]

Formal-Resource-Based techniques are referring to highly structuredknowledge (so-called Background Knowledge). The formalresources can be upper-level taxonomies, which are summarizingdifferent domains in one repository, or domain-specific taxonomies,i.e. standard taxonomies, or resources published as linked data.

• Domain-Specific taxonomies are representing conceptsbelonging to part of the world [Otero-Cerdeira et al., 2015], e.g.the standard taxonomy North American Industry ClassificationSystem (shortened: NAICS) is describing customer groups ofdifferent domains [NAICS Association, 2007].





Formal-Resource-Based [2]

• Upper-Level taxonomies are representing the same domain asthe taxonomies, but in a more generalized way, e.g. the NorthAmerican Product Classification System (shortened: napcs)describes product groups in a very general way[Mohr and Russel, 2002].

• Linked Data is the paradigm of structuring and publishing datafor the Semantic Web, e.g. the service Freebase publishesdatabases for describing diverse artifacts[Bizer et al., 2009, Bollacker et al., 2008].





Informal Resource-Based [1]

Informal Resource-Based techniques are also exploiting thestructured background knowledge in directories or annotations.However, now the external directories are informal ones.

• Directories are informally structured indexes, e.g. the InternetRetailing21 directory, or the Yahoo!22 directory are informal webdirectories.

• Annotations are any types of resources having furtherdescriptions.

21http://internetretailing.net/22https://local.yahoo.com/


http://internetretailing.net/

https://local.yahoo.com/




String-Based/Lexical-Based [1]

Techniques measuring correspondences based on the equality ofcharacters are called String-Based or Lexical-Based techniques[Cheatham and Hitzler, 2013]. Such techniques aim in findinghomogeneity between the labels used to distinguish betweenconcepts, and/or between its descriptions. The similarity can beevaluated against three types of sequences and result in a distancematrix.

Name Similarity means to measure the similarity of single words ormulti-words. The most well known name-similarity measures are:

• Levenshtein Distance characterizes the minimum number ofedits required to transform one string into the other[Levenshtein, 1966]. Each edit is qualified by the kind oftransformation necessary for each character: if a single characterhas to be inserted, deleted, or substituted.






• Euclidean Distance depicts the length of the connectionnecessary to combine one point in the Euclidean space withanother point. Hereby, each character of a string is assigned to apoint in the Euclidean space [Bailey, 2004].

• The Hellinger Distance depicts an alternative to the Euclideandistance but uses a probability density [Hellinger, 2009].

• Hamming Distance describes the number of characters beingdifferent at the same index. Each substitution necessary totransform the initial character into the target character increasesthe distance result [Li and Jain, 2009].






• Shortest Path measure is the length of the shortest pathbetween two nodes [Lee, 1958, Rada et al., 1989]. Similar toHamming distance, the strings must have the same length.

• Lin Distance is measuring the probability that a string occursinside a label [Kernighan and Lin, 1970].

• Wu-Palmer measure is classifying each label according to itsdepth inside a comparable text corpus [Wu and Palmer, 1994].

• Naive Bayes Classifier measures the probabilities under whicha document in a class occurs. It does not take into account theimportance of the words [Friedman et al., 1997].






Description Similarity takes into account compound terms to becompared with another sequence. Some importantdescription-similarity measures are:

• TF-IDF stands for Term Frequency Inverse DocumentFrequency, and states how important a word is [Jones, 1972].The value increases by the number of occurrences in thedocument.

• Jaccard Distance is representing the resemblance between twosets of strings. The maximum coefficient is one, if the two setsare identical (for details see [Tan et al., 2005]).

• Cosine Similarity considers the sequences as vectors[Tan et al., 2005].

Global Namespace considers the similarity between twonamespaces.





Language-Based [1]

Language-Based techniques are considering the equality of thewords used to classify concepts, and are thus often combined withstring-based methods. Such techniques are taking into accountsequences of text, which are broken into meaningful elements to becompared. The comparison is predominantly supported with theinclusion of background knowledge. Six categories exist to distinguishbetween the types of language-based methods:

• Tokenisation breaks a stream of text into words, phrases,symbols, or other meaningful tokens. For example, the streamwhat’re can be tokenized into what and are. Some recentalgorithms techniques do not break the stream into single wordsas single tokens, as often two words can belong together, e.g.the stream This is a car is broken into This is and a car. Thismethod is named N-Gram, whereby the N depicts the number ofwords assigned to a token.





Language-Based [2]

• Lemmatisation is grouping together the different inflected formsof a word so they can be analyzed as a single item, e.g. the wordbetter has good as its lemma.

• Morphology analysis the inner structure of a given language’ssmallest grammatical unit, e.g. the word SUV is an abbreviationfor Sport Utility Vehicle.

• Elimination reduces the tokens with any elements considered assuperfluous, e.g. the stop-word and, or quotation marks are oftenremoved in recent strategies.

• Lexicons are used to translate between languages. The mostwidely-used lexicons, also named translators, are the MicrosoftBing23 translator, or the Google24 translator.





Language-Based [3]

• Thesauri are utilized to analyze semantic similarity betweenconcepts, e.g. the lexical database WordNet[Fellbaum, 1998, Miller, 1995]. In WordNet, a synset, i.e. set ofsynonyms, is presented for every word sense with one or moredifferent word(s), and a class type for each word, which can bean adjective, adverb, noun, or verb. As WordNet is also ataxonomy, it helps to deduce conceptual similarities by the help ofsemantic relationships. The most important relationships are thehypernym relationships, i. e. super concept, hyponymrelationships, i.e. sub concept, and the sibling term relationships,i. e. sibling concept. Other relationships, which are not providedfor all synsets are antonym relationships, i.e. opposite meaning,and meronym relationships, i.e. part of.





Language-Based [4]

• Word Sense Disambiguation is utilized to analyze the sense ofthe sentence in the recent context, i.e. the most importantsequence or token of the stream. For example, for the stream Weare driving car, driving is the sense of the sentence.





Constraint-Based [1]

Constraint-Based techniques are analyzing the internal structure ofthe taxonomy. For the identification of similarity, such methods areconsidering the criteria used to structure the out-tree. The relationsbetween the variables are stated in the form of constraints to analyzethe correspondences.

• Type Similarity of the attributes is considered because of thiselements are describing the concepts of a domain. Thus, twoconcepts sharing the same types, for example Sedan andLimousine are sharing the attributes Color, Luggage Space, andNumber of Doors, can be assumed to be semantically similar. Oron the other site, when the attributes are terminologically differentbut hold the same meaning, for example Varnish and Color,concepts using this attributes can be considered as semanticallysimilar.





Constraint-Based [2]

• Key Properties are used to describe the concepts belonging to ataxonomy, e.g. the concepts can be classified after the Sizes ofthe different Cars, or the Price of the different Cars. When theconcepts inside the taxonomies are structured according to acorresponding point of view, the taxonomies can be assumed tobe similar.





Taxonomy-Based [1]

Taxonomy-Based techniques are focussing on the is-a relationshipsinside the taxonomy. There is no further classification provided in theliterature for this techniques.

• Taxonomy-Structure means the structure of the two out-trees tobe compared. The taxonomies can differ in the total number ofconcepts, in the number of is-a relationships, and in the numberof sibling concepts for each super concept. The less different thestructure of two concepts are, the more semantically similar bothare.





Graph-Based [1]

Graph-Based techniques consider the taxonomies as labelledgraphs. In contrast to the techniques mentioned above, now also thesibling relationships are taken into account allowing to compare setsof sub concepts and the distance between paths. Four directions ofgraph-based techniques can be distinguished:

• Graph-Homomorphism measures the relationships betweennodes having different structures. For example, the graph G1 =({1, 2, 3, 4}, {1, 2}, {1, 2}, {2, 3}, {2, 3}, {2, 4}, {3, 4}) has thesame relationships as G2 = ({1, 2, 3, 4}, {1, 2}, {2, 3}, {3, 4}, {2,4}).

• Path Similarity measures the similarity depending on the seriesof edges used to connect following nodes. For example, G1 usessix edges, G2 only four.





Graph-Based [2]

• Children Similarity stands for the number of outgoing edges of anode. For example, the edge 1 in the graph G3 = ({1, 2, 3}, {1,2}, {1, 3}) has two children, the edge 2 and 3.

• Leaves Similarity is comparing the common point between twoedges, inside a graph representing the out-tree. For example, thegraph G1 has five vertex, the graph G2 only four.





Instance-Based [1]

Instance-Based techniques are indicating the similarity betweenconcepts depending on the instances (e.g. products, sub concepts)assigned to concepts. The similarity depends on the two sets to becompared, because similar concepts should have similar instances.

• Data Analysis and Data Statistics means to compare the setsof instances or properties assigned to a concept. For exampleX1, X2 and X3 are assigned to the concept Coupe, and X1, X2and X4 are assigned to the concept Two Seaters, so there mustbe a series between the three instances. Consequently, suchmethods are supported by other techniques to derive thesimilarity of the sets, e.g. with the Cosine or Jaccard distance.





Model-Based [1]

A few approaches have taken into account the description logics forovercoming taxonomy heterogeneity as well as the satisfiability,named Model-Based Techniques.

• Satisfiability Solvers determine if there exists an interpretationsatisfying a given Boolean operator, which can be true or false.

• Description Logics reasoner is a family of formal knowledgerepresentation languages. A reasoner is a technique that is ableto infer logical consequences from a set of entities.




Practice - Basics of Artificial Intelligence

Basics Artificial IntelligencePractice - Basics of Artificial Intelligence





General Instructions [1]

The practical exercises presented on the following pages require adifferent amount of time. For that reason, a recommendation(so-called time-box) regarding how many time should be enough tospent on the accompanying exercise is given. However, independentfrom the time to be spent, each exercise consists of two parts:

i. The first part is the elaboration.

ii. The second part is the presentation.

A task is not fulfilled if one of the parts is missing. The presentationshould be held in front of audience of the course. If the exercise isperformed by a group, the presentation should be performed by allmembers of the group, whereby not all members must give thepresentation. The time for the presentation is included in the time-box.A group should consist of maximum three students – unless otherwisestated.





Basics Artificial Intelligence, Exercise 1 (90 min.) [1]

1. Get the article “Computing Machinery and Intelligence”.

2. Get the article “Turing and Artificial Intelligence”.

3. Read both above-mentioned articles.

4. Summarize your key findings.






1. Find three recent Chatbot applications.

2. Try communication with each bot.

3. Find an online implementation of the ELIZA computer program.

4. Compare the three recent bots with ELIZA.

5. Justify whether ELIZA has passed the Turing test or not.






1. Create a knowledge base to structure the different academicroles working at an university.

2. Justify which techniques and methods you have used to developthis knowledge base.

3. Justify which semantic relationships between the roles exist.

4. Justify if your knowledge base is complete.






1. Look at the product taxonomy of the online-shop by Amazon (usean excerpt of the German site).

2. Look at the product taxonomy of the online-shop by Alibaba (usean excerpt the English site).

3. Summarize the matches between both e-catalogs.

4. Summarize the differences between both e-catalogs






1. There are two agents: Agent 1, and Agent 2.

2. Each agent has to classify if a product is relevant or non-relevantfor a customer.

3. Agent 1 classifies 25 products as relevant that are actuallyrelevant, 75 products as irrelevant that are actually irrelevant, 50products as relevant that are actually non-relevant, and 25products as non-relevant that are actually relevant.

4. Agent 2 classifies 50 products as relevant that are actuallyrelevant, 65 products as irrelevant that are actually irrelevant, 40products as relevant that are actually non-relevant, and 20products as non-relevant that are actually relevant.

5. Identify, which agent performs better.



Artificial Intelligence Market







• Knowing to what extent Artificial Intelligence technologies arerecently used in companies.

• Knowing some companies that exist in the field of ArtificialIntelligence, and their products.

• Knowledge about which Artificial Intelligence technologies areused in daily life in various industry sectors.

• Critical examination of the topic complex Artificial Intelligence, itsrecent applications, and its current relevance




General relevance Artificial intelligence

Artificial Intelligence MarketGeneral relevance Artificial intelligence





Discussion: General relevance Artificial intelligence [1]


• Who is familiar with the term Artificial Intelligence?

• How do you use Artificial Intelligence in privacy?

• What potential do you see for Artificial Intelligence technologies?

• What hazards and negative effects do you see about ArtificialIntelligence technologies?





Expectations and Anxiety [1]

Apparently, with the advent of Apple’s voice service Siri25, or thedevelopment of Amazon’s assistance system Alexa26, the “broadmass” now (finally) has access to “intelligent” technologies.

Such techniques are merchandised under the buzzword ArtificialIntelligence (shortened: AI)27.

AI






Expectations regarding future technologies are enormous. As are thepotential fears for individuals, businesses, etc.:

• Will machines take over our work in the future and will thesemachines take away our jobs?

• Will the machines make our jobs and more simple and bearable?

• Will we soon be dominated by machines?

• How safe and private can we live in the future, if machines soonknow everything?






• Is my data in danger?

• What does the government know about me?

• How much do companies know about me?

• How can it be that a retailer knows what I need?

• When can we talk with robots?

• How far are other countries in terms of future technologies andmay new threats emerge?






The German government has now also recognized the importanceof this topic and meets the importance of artificial intelligence forindividuals and businesses in Germany with a correspondingpublication in November 2018:

“Strategie Künstliche Intelligenz derBundesregierung”;

The letter describes the current situation and outlines areas of actionin the field of artificial intelligence. According to recent studies andconsulting houses, the publication by the German government cametoo late. Germany has already taken on a supporting role and leavesthe US and China very clear and conspicuous the lead[PricewaterhouseCoopers International, 2019].






According to the platform Lernende Systeme28 (English: LearningSystems), there are 600 projects, providers or users in the area of AIin Germany:

• About 300 projects of research institutions exist.

• About 150 start-ups in the field of AI exist;

• About 120 small, middle, and large companies in the field of AIexist;






According to another platform, Crunchbase29, about 350 projects andstart-ups exist in Germany. Even if the resources differ, this is nottraumatic. The reason is that it highlights that Germany is far behindother countries, especially behind the following countries:

• In the USA, 4,000 start-ups exist according to Crunchbase.

• And, China has the goal to overtake the leading role of the USAin the next years.

Germany is far away from a leading role in artificial intelligence.

25https://www.apple.com/en/siri/26https://developer.amazon.com/en/alexa27https://www.technologyreview.com/artificial-intelligence/28https://www.plattform-lernende-systeme.de/ki-landkarte.html29https://www.crunchbase.com


https://www.apple.com/en/siri/

https://developer.amazon.com/en/alexa

https://www.technologyreview.com/artificial-intelligence/

https://www.plattform-lernende-systeme.de/ki-landkarte.html

https://www.crunchbase.com




General Significance [1]

Meanwhile, many institutions, institutions, as well as privateindividuals and companies in Germany deal with the field of AI. It isabout the development and application of existing technologies, butalso about inspiration for new applications. If one compares the termAI with other established disciplines of computer science with respectto their current general significance, the result is very clear.

For example, if the German translation for the search term “ArtificialIntelligence” (German: “Künstliche Intelligenz”) are compared with theGerman translation for the search terms “Theoretical ComputerScience” (German: “Theoretische Informatik”) and “TechnicalComputer Science” (German: “Technische Informatik”).






The results when comparing the above-mentioned search termsaccording to their relevance during the last ten years is as follows30

(see Figure 24):

• In 2008, the terms were approximately evenly distributed. TheGerman translation for the term “Artificial Intelligence” achieved48 %, the German translation for the term “Theoretical ComputerScience” 35 %, and the German translation for the term“Technical Computer Science” achieved 52 %.

• In 2018, the terms were strongly different distributed. TheGerman translation for the term “Artificial Intelligence” achieved60 %, the German translation for the term “Theoretical ComputerScience” 6 %, and the German translation for the term “TechnicalComputer Science” achieved 8 %.






2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 20180

20

40

60

80

100

Year

Rel

ativ

ese

arch

inte

rest

Interest over time

Künstliche IntelligenzTheoretische InformatikTechnische Informatik

Figure 24: Arithmetic mean of the relative frequency of search input for different searchterms from year 2008 to 2018 (resource and search engine used: Google).

30https://trends.google.de


https://trends.google.de




Areas of Everyday live Applications [1]

AI seems to be indispensable in our private lives. Some importantdirections of current day-to-day technologies strongly influence aredaily live:

• The way of how we are communicating.

• The way of how we find and consume things.

• The way of how we operate safety/security.

• The way of how we operate traffic.

• The way of how we stay healthy.





Everyday live Applications – Communication [1]

In terms of communication, AI technologies, in particular, provideautomation, media neutrality and accessibility:

• Automation of communication, and communication acrossdifferent input channels. For example, when buying something ina local grocery store and paying using Amazon’s Apple Pay.

• Executing actions using parts of body (e.g. eyes, finger print,voice, sweat, heat). For example, when talking to recentassistance systems like Apple’s Siri31 or Amazon’s Alexa32.

• Automatic translation of texts (e.g. multilingualism, semanticcontext translations). For example, when using the translationplatform DeepL33 provided by Linguee.

31https://www.apple.com/en/siri/32https://developer.amazon.com/en/alexa33https://www.deepl.com


https://www.apple.com/en/siri/

https://developer.amazon.com/en/alexa

https://www.deepl.com




Everyday live Applications – Consumption [1]

Regarding consumption (e.g. trade, retail), AI technologies enablequickly finding products, services, games, and entertainment:

• Software that enables direct communication and interaction. Forexample, when talking to a chatbot as provided by the companyPandorabots, named Mitsuku34.

• Filtering of product reviews and digital assistance systems (e.g.dynamic taxonomies, faceted search). For example, when usingthe online shop Alibaba35.

• Finding and streaming of entertainment (e.g. audio streaming,video streaming, literature search). For example, when using theonline streaming service Netflix36.

34https://www.pandorabots.com/mitsuku/35https://www.alibaba.com36https://www.netflix.com


https://www.pandorabots.com/mitsuku/

https://www.alibaba.com

https://www.netflix.com




Everyday live Applications – Security [1]

In terms of safety, AI technologies enable preventive measures usingimage and pattern recognition, e. g.:

• Automatic face recognition using infrared. For example, whencomparing two photos for logging on to your smartphone, asprovided by the Microsoft demo37.

• Detection of riots, e.g. to predict and prevent political riots.

37https://aidemos.microsoft.com/face-recognition


https://aidemos.microsoft.com/face-recognition




Everyday live Applications – Traffic [1]

AI technologies significantly affect the kind and realization of mobility,as well as its support and automation. The main focus in research isrecently on autonomous driving including the following problems:

• Automatic parking, as provided by Daimler ’s serviceDrop-Off-Area38.

• Driving assistance in terms of speed, distance control, dangeridentification, and breaking, as provided by Volkswagen’stechnology Active Cruise Control 39.

38https://www.daimler.com/innovation/case/autonomous/fahrerlos-geparkt.html

39https://www.volkswagen-newsroom.com/de/automatische-distanzregelung-active-cruise-control-acc-3664


https://www.daimler.com/innovation/case/autonomous/fahrerlos-geparkt.html

https://www.daimler.com/innovation/case/autonomous/fahrerlos-geparkt.html

https://www.volkswagen-newsroom.com/de/automatische-distanzregelung-active-cruise-control-acc-3664




Everyday live Applications – Health [1]

With regard to health, artificial intelligence technologies enable, inparticular, diagnostic support methods and assistance systems:

• Recognition of disease, for example, with the system namedIDx-DR40 provided by IDx.

• Analysis of symptoms for disease recognition.

One main focus in research is recently on the recognition of cancer,as well as methods for dealing with Alzheimer disease.

40https://www.eyediagnosis.co/idx-dr-eu-1


https://www.eyediagnosis.co/idx-dr-eu-1



Industrial Applications Artificial Intelligence

Artificial Intelligence MarketIndustrial Applications Artificial Intelligence





Discussion: Industrial Applications Artificial Intelligence [1]


• Which technology suppliers in the field of Artificial Intelligence doyou already know?

• Which artificial intelligence technologies do you know fromeveryday working life?

• Are you maybe involved in the development of artificialintelligence technologies?

• Would you like to participate in the development of artificialintelligence technologies?

• Which artificial intelligence technologies do you wish to facilitateyour professional life?





AI examples by SAP [1]

SAP SE (shortened: SAP) is the biggest software manufacturer inEurope. The company was founded 1972 and is located in Germany.The company develops software for executing business processes:

• The most known product is the software package SAP ERP41

• Additionally, they provide other technologies, e.g. the databasesolution SAP S/4HANA42, as well as the e-commerce solutionSAP Commerce Cloud43.





AI examples by SAP [2]

The innovation strategy by the company concentrates on thedevelopment of new markets, as well as on the permanent furtherdevelopment of existing technologies. An important role for innovationis the technology focus AI.

The main area of AI that is considered is Machine Learning. In detail,the development of innovative machine learning approaches, and its’trouble-free integration to the product portfolio of thefirm[Leukert et al., 2018].

41https://www.sap.com/germany/products/erp.html42https://www.sap.com/germany/products/s4hana-erp.html43https://www.sap.com/germany/products/crm/e-commerce-platforms.

html


https://www.sap.com/germany/products/erp.html

https://www.sap.com/germany/products/s4hana-erp.html

https://www.sap.com/germany/products/crm/e-commerce-platforms.html

https://www.sap.com/germany/products/crm/e-commerce-platforms.html




AI examples by SAP: Robotic Process Automation [1]

Repetitive processes in a firm can be automized using SAP ’stechnology Shared Service Centers. One good example for usingthe product is the assignment of incomes to an invoice.

The included service named SAP Cash Application44 elaboratessuggestions for a quicker assignment of open positions and incomingpayments. The application uses data of past assignments, wherebythe assignment is trained. The model is using data about customersand countries. Using this, an allocation rate is created. The ratedetermines how strong two criteria are similar, and how strongassignment criteria are fulfilled. The model works in asemi-automatically way when the assignment criteria are not clear.Then assignment suggestions are performed. If the criteria are clear,the model works fully-automatically.

44https://www.sap.com/germany/products/cash-application.html


https://www.sap.com/germany/products/cash-application.html




AI examples by SAP: Customer Relationship Management [1]

The optimization of the customer experience is increasingly important.The reason is the digital transformation. The interaction between firmsand customers has changed.

Nowadays, customers want to inform from any place and time aboutoffers. An excellent service is expected, as well as the possibility toconnect to the service provider by using different channels andtouch-points (mail, app, homepage, e-shop, etc.). The diversity ofrecent digital touch-points provides the basics for the extraction of newinsights about the customer behavior. Machine learning models canresult personalization actions and improve the customer experience,as provided by SAP ‘s SAP Hybris Marketing Cloud45.






Main goal of marketing is to provide a fitting offer to a potentialcustomer so that she/he buys the product. To allow this, the offer shallcontain the optimal prize and include appealing assets (videos, text,images, etc.). Of course, the by the customer preferred channel shallbe used to offer the product. The right strategy, price and channel canbe elaborated by the machine learning model. SAP ’s framework SAPBrand Impact46 offers a solution, which companies use for automaticreal-time analysis of film, video and television content to measuretheir brand image and impact.






Similar to marketing, service and sales are all about targetingcustomers. Sales systems based on machine learning models allowservice and sales employees to read important customer informationbased on historical data. SAP ’s solution SAP Hybris Cloud forCustomer47 automatically determines the sales opportunities for alead and automatically maps a customer request based on historicaldata to the appropriate employee of the correct service team througha ticket. This simplifies the mapping process and allows the system toprovide solutions to queries. Especially the latter, i.e. the interactionbetween two input sources will become more important with chatbots.They can answer questions from customers in natural language, bothduring and after opening hours, instead of having to navigate to anappropriate source of information.

45https://www.sap.com/germany/products/crm/marketing.html46https://news.sap.com/2017/05/sapphire-now-sap-brand-impact-video-analytics-measure-brand-exposure/47https://www.sap.com/germany/products/cloud-customer-engagement.

html


https://www.sap.com/germany/products/crm/marketing.html

https://news.sap.com/2017/05/sapphire-now-sap-brand-impact-video-analytics-measure-brand-exposure/

https://www.sap.com/germany/products/cloud-customer-engagement.html

https://www.sap.com/germany/products/cloud-customer-engagement.html




AI examples by SAP: Human Resource Management [1]

Human resource management has evolved over the last two decadesinto a system that puts the focus on the employee. In particular, it isabout the commitment of the employees, a fair and social workingculture as well as an open feedback environment.

Advanced on Machine Learning based forecasting andrecommendation technologies such as SAP ’s SAPSuccessFactors48 can help shift the focus from time-consumingprocesses to developing growth-enhancing HR strategies. Possibleother important fields of application of ML-based technologies inhuman resources management are the more efficient and objectivehandling of recruitment processes as well as the continuousdevelopment of employees through a strong learning culture.

48https://www.successfactors.com/de_de/solutions.html


https://www.successfactors.com/de_de/solutions.html




AI examples by Amazon [1]

Amazon.com, Inc. (shortened: Amazon) is a leading mail ordercompany based in the USA. The company was founded in 1994 todistribute books and currently offers a wide range of products andservices.

Some well-known services are the online shop AmazonMarketplace49, the multi-service Amazon Prime50, the paymenttechnology Amazon Pay51https://pay.amazon.de, the donationservice Amazon Smile52, or the marketing tool AmazonAdvertising53.





AI examples by Amazon [2]

For the company, Germany is an attractive location for AI experts fromall over the world, which is why the company employs an internationalteam in Germany. Since its inception in 2013, the AmazonDevelopment Center GmbH has not only supported the developmentof operating systems, management tools, and other applications forAmazon Web Service’s, but also the development of AI technologies.

49https://www.amazon.de/50https://www.amazon.de/amazonprime51\unskip\penalty\@M\vrulewidth\z@height\z@depth\dp¸52https://smile.amazon.de53https://advertising.amazon.de/


https://www.amazon.de/

https://www.amazon.de/amazonprime

\unskip \penalty \@M \vrule width\z@ height\z@ depth\dp

https://smile.amazon.de

https://advertising.amazon.de/




AI examples by Amazon: Supermarket without cask [1]

During the shopping a lot of time is spent waiting. This especiallyimmediately before and after the payment process and the selection.For example, the storage of the goods in the shopping cart, thesubsequent storage of the goods on the cash register and the finalsorting of goods again in the shopping cart or in shopping bags.

The concept of Amazon’s Amazon Go eliminates the hassle ofpacking and unpacking in one step, the former. There is no manualand time-consuming payment process, the payment is purely digital.The technological effort for this, however, is considerable. It requires avariety of sensors and combinations that make the payment smoothlyand correctly.





AI examples by Amazon: Package delivery without parcelservice [1]

Especially in sparsely populated areas or in hard-to-reach areas,customers are often denied the option of a speedy delivery. Packetservices rarely deliver, but other merchants may be a long way away.

Amazon counters this problem with drone delivery. In this way,Amazon wants to be in a position to offer customers in sparselypopulated areas a particularly fast delivery, e.g. as integration intoservice Prime-Now. The technological effort is enormous for this,because in particular the criteria reliability, security as well as legalrequirements play a serious role.





AI examples by Amazon: Sorting fruits [1]

Freshness is an important aspect especially when buying food online.In contrast to local supermarkets, the freshness can not be assessed,for. B. by watching or feeling. A concrete field of application forArtificial Intelligence at Amazon is an automated system that candetermine the degree of ripeness of fruit and vegetables with the helpof an ML model. This increases the reliability of online groceryshopping and reduces the food exclusion rate.

The system developed by Amazon, which uses Amazon Fresh54, isnow better than the human eye and does not have to touch the fruit orvegetables. The system has sensors and is constantly training in theassessment and categorization (categories: ok, damaged, badlydamaged, expired) of fruits and vegetables.

54https://www.amazon.de/fresh


https://www.amazon.de/fresh




AI examples by Amazon: Demand forecast for products [1]

That at Christmas time there is a high demand regarding treedecorations and fairy lights is just as comprehensible as theOktoberfest customers in the Munich area especially demandtraditional clothing. To be able to respond to these trends, the systemmust plan ahead, that means Amazon needs to know ahead of timehow many customers will be in demand in certain regions to avoiddelivery bottlenecks. Amazon uses data from the past to makepredictions for the future. The system does not look at individualcustomers but aggregates via data about a delivery region or thebuying behavior of different customer groups. Logically, the systemlearns with each new order and with every right or wrong forecast.





AI examples by Amazon: Smart language assistant [1]

Amazon’s chatbot is available in the cloud and integrated withAmazon Echo55. The purpose of Alexa is to receive assistance in theorganization of everyday life by means of voice commands. In order toprovide the answers to questions and instructions, the system useslanguage examples in which each syllable and every letter isevaluated.

The system uses various information sources on the Internet (egWikipedia). The combination of correctly capturing the language andretrieving the right source of information requires thousands oflearning processes to distinguish, for example, between differentlanguages and between different dialects, as well as the underlyingcontext of the input.

55https://www.amazon.de/amazon+echo


https://www.amazon.de/amazon+echo




AI examples by Walmart [1]

Walmart Inc. (shortened: Walmart) is known as a digital affine tradingcompany and the world’s largest retailer in the USA. Among otherthings, the company experimented with virtual reality headsets inemployee training or presented a prototype for Virtual Realityshopping.

The most successful experiment by Walmart was the use of theshelf-scanning robots [Gläß, 2018]. The approximately 65 cm highmoving robots have a shelf-high camera column and patrollingindependently through the aisles of the shops. They scan the shelvesand recognize when items are out of print, in the wrong place orwrongly labeled. You report the errors to the next employee, who thenfixes them manually. The robots are about 50 % more productive thantheir human counterparts and scan the shelves with higher precisionin just one third of the time previously required.





AI examples by Otto [1]

Otto GmbH & Co. KG (shortened: Otto) is one of the most successfule-commerce companies in Europe today. Otto uses AI in particular toimprove the shopping experience of end customers[Gläß, 2018].

The main factors here are the availability of goods, the supply and thedelivery time as well as customer satisfaction at the varioustouch-points of the customer journey. Here, the AI must understandwhat is relevant at which point in time for the individual customer. TheAI analyzes customers with similar behavior and forecasts theprobability of purchase and the need situation. Also, AI is used toplace product recommendations and customize search results. Forthis purpose, customer evaluations are used, which are analyzed bymeans of the mentioned aspects and tonality. This allows customersto filter the most important ratings for you and reduces the likelihoodof a purchase.





AI examples by Lidl [1]

Lidl Stiftung & Co. KG (shortened: Lidl) is one of the largestdiscounter companies in the world.

In the field of logistics, Lidl cooperates with the company Vanderlandeand converted its distribution center into a fully automated high-baypallet warehouse. This system solution now enables fully automaticdepalletizing and enables automated picking of thegoods[Gläß, 2018].




Recent market situation Artificial Intelligence

Artificial Intelligence MarketRecent market situation Artificial Intelligence





Discussion: Recent market situation Artificial Intelligence [1]


• Which pioneering countries regarding Artificial Intelligencetechnologies do you know?

• Which industries do you think have the greatest potential forusing Artificial Intelligence technologies?

• What is the most important criterion and immediate requirementfor the implementation of Artificial Intelligence technologies?

• What areas of Artificial Intelligence do you know?





Market research in the area Artificial Intelligence [1]

AI is present in society, in politics, in the media, in the sciences and ina wide range of economic sectors.

Meanwhile, there are also a variety of sources and publications thatexplore the field of AI in terms of its use, applications and potential inan industrial context. A good source for studies with an industrialfocus rather than a scientific focus are market research institutes,consulting firms and networks.





Market research in the area Artificial Intelligence [2]

Some interesting institutions and resources are:

• The strategy consulting house McKinsey & Company56.

• The network PricewaterhouseCoopers International57.

• The consulting house Roland Berger Holding GmbH58.

• The management consultant house Accenture Plc59.

• The network Ernst & Young Ltd.60.

• The strategy consulting house Boston Consulting Group61.

• The strategy consulting house Deloitte62.

56https://www.mckinsey.de57https://www.pwc.de58https://www.rolandberger.com59https://www.accenture.com61https://www.bcg.com62https://www.deloitte.com


https://www.mckinsey.de

https://www.pwc.de

https://www.rolandberger.com

https://www.accenture.com

https://www.bcg.com

https://www.deloitte.com




The usage of Artificial Intelligence in Germany [1]

In Februar 2019, the strategy consulting house McKinsey &Company63 (shortened: PwC) has published a study about AI incompanies, with a focus on the German market[PricewaterhouseCoopers International, 2019]. The most importantinsights are as follows:

Germany and German companies are currently not or veryslightly oriented towards the topic of AI.

ccording to the mentioned, States such as the USA or Chinahave clearly assumed a pioneering role in the AI technologymarket. These countries have the highest density of start-ups inthe AI environment and invest the most money.

The main insights are summarized on the following slides.






The most important criterion for AI is data. A possible evolutionarystage of AI in companies is therefore software-controlled processautomation, named Robotic Process Automation (shortened: RPA).

RPA serves to automate repetitive tasks and is therefore the basicsupplier of data. According to the study by PwC, 61 % of companiesin Germany currently do not use RPA at all or do not know at all whatRPA is (see Figure 25). This means that many German companies donot even have the basis to operate or use AI.






0 5 10 15 20 25 30 35 40 45 50 55 60

yes

no

do not know

39

59

2

Figure 25: Distribution of software-supported process automation in Germancompanies [PricewaterhouseCoopers International, 2019].






Logically, the study also revealed that only a very small proportion ofrespondents currently use AI. Only 23 % of respondents used AI, areusing it or are planning to use it.

The most frequently used and imaginable AI applications are dataanalysis, RPA and chatbots. In contrast, fewer respondents alreadyuse AI components in products or services.

Consequently, companies also see data as the most important factorinfluencing the successful use of AI. Closely followed by thechallenges of compliance and AI competencies.

In general, the AI is predominantly seen as a support for human work,rather than as autonomous systems.






0 5 10 15 20 25 30 35 40 45 50

not specific

AI not relevant

AI considered relefant

planning phase

testing phase

implementation phase

usage

1

48

28

14

3

2

4

Figure 26: Phases of AI use in German companies[PricewaterhouseCoopers International, 2019].






0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75

Analytics/Data Analysis

Robotic Process Automation

Chatbots

Digitale Business Models

Speech Processing

Products and Services

Others

70

63

47

44

42

39

4

Automatisierungspotential der Aktivitäten

Figure 27: Use of AI applications in German companies[PricewaterhouseCoopers International, 2019].






35 40 45 50 55 60 65 70

Data

Compliance

Competencies/SkillsData Pool

Trust (Customers)

Trust (Stakeholders)

CompetitivenessPersonal Trust

AI Governance

Business Model

Reputation

69

59

59

55

54

53

49

47

43

40

35


Figure 28: Importance of different factors in the use of AI for German companies[PricewaterhouseCoopers International, 2019].






5 10 15 20 25 30 35 40 45 50 55 60 65 70 75

support human work

autonomous acting system

unclear

71

20

9


Figure 29: Expected degree of autonomy of AI systems[PricewaterhouseCoopers International, 2019].

63https://www.mckinsey.de






Interesting branches of industry in the field of AI [1]

In April 2017, the strategy consulting house McKinsey & Company64

(shortened: McKinsey), has published a study about the usage of AIfor the German industry sector [McKinsey & Company, 2019]. Hereby,they analyzed the degree of possible automation for various sectors ofthe economy with regard to the following areas:

• Management and development of persons;

• Application of expertise for the making of decisions;

• Communication with stakeholders;

• Execution of physical unpredictable activities;

• Execution of physical foreseeable activities;

The main insights are summarized on the following slides.






According to the study, the latter three areas in particular have a highpotential for the use of artificial intelligence. In particular, theindustries with a high rate of physically predictable activities have thehighest potential for the use of Artificial Intelligence technologies. Thefive branches of the economy with the highest degree of possibleautomation are (see Figure 31):

1. Accommodation and meals (72 % automation potential);

2. Transportation and storage (64 % automation potential);

3. Agriculture (60 % automation potential);






30 35 40 45 50 55 60 65 70 75

Accommodation and mealsTransport and storage

AgricultureRetail trade

ManufactureInformationWholesale

Other servicesArt and Entertainment

Real estateAdministration

ConstructionFinance and insurance

MiningUtilities

AdministrationHealth

JobEducation

7264

6056

555151

4848

474646

4443

4238

3634

32

Automation potential of the activities

Figure 30: Technical potential for automation by sector of activity[McKinsey & Company, 2019].






McKinsey also examined the technology areas with the highestpotential in terms of the development and use of KI technologies. Itcompared the use cases and the industry sector and examinedwhether this will have a high, medium or low impact.






The application cases and industries with high influence are (seeTables 6, 7, 8, 9, and 10):

• Autonomous driving in the air & space travel, in the commercialcontext as well as in the area of supply;

• Foresighted maintenance in the commercial context and for theindustrial equipment;

• Context sensitive robots for industrial equipment;

• Efficiency increase manufacturing area using semiconductors;

• Improvement of the supply chain through autonomous supplyand industrial equipment;

• Semiconductor research and development;






Table 6: Comparison of application case in the industrial area of Aerospace regardingthe use and development of KI technologies.

Application AerospaceAutonomous driving highForesighted maintenance mediumContext sensitive robots mediumEfficiency improvement manufacturing lowEfficiency improvement Quality assurance mediumImprovement of supply chain mediumResearch and development mediumBusiness Function Support low






Table 7: Comparison of application case in the industrial area of Autonomous Vehiclesregarding the use and development of AI technologies [McKinsey & Company, 2019].

Application Autonomous VehicleAutonomous driving highForesighted maintenance highContext sensitive robots mediumEfficiency improvement manufacturing lowEfficiency improvement Quality assurance mediumImprovement of supply chain mediumResearch and development mediumBusiness Function Support medium






Table 8: Comparison of application case in the industrial area of Autonomous Supplyregarding the use and development of AI technologies [McKinsey & Company, 2019].

Application Autonomous SupplyAutonomous driving highForesighted maintenance mediumContext sensitive robots mediumEfficiency improvement manufacturing lowEfficiency improvement Quality assurance lowImprovement of supply chain highResearch and development mediumBusiness Function Support low






Table 9: Comparison of application case in the industrial area of Industrial Equipmentregarding the use and development of AI technologies [McKinsey & Company, 2019].

Application Industrial EquipmentAutonomous driving lowForesighted maintenance highContext sensitive robots highEfficiency improvement manufacturing mediumEfficiency improvement Quality assurance mediumImprovement of supply chain highResearch and development lowBusiness Function Support low






Table 10: Comparison of application case in the industrial area of Semiconductorsregarding the use and development of AI technologies [McKinsey & Company, 2019].

Application SemiconductorsAutonomous driving mediumForesighted maintenance mediumContext sensitive robots lowEfficiency improvement manufacturing highEfficiency improvement Quality assurance lowImprovement of supply chain lowResearch and development highBusiness Function Support Medium






The PricewaterhouseCoopers International (shortened: PwC)network has also conducted a study to determine which AIapplications are used or conceivable[PricewaterhouseCoopers International, 2019]. To this end, 500companies were surveyed, of which about half of the companies areAI-affine (255) and about half are not AI-affine. The three areas withthe highest degree of use are:

• Analytics/data analysis for decision-making processes;

• Process automation of existing business processes;

• Chatbots;






0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75

Analytics/data analysis for decision-making processes

Process automation of existing business processes

Chatbots

Digital business models

Speech processing

products and services

Other

70

63

47

44

42

39

4

Relative share of use.

Figure 31: Used and imaginable applications from the field of AI[PricewaterhouseCoopers International, 2019].

64https://www.mckinsey.de





Practice - Applications of Artificial Intelligence

Artificial Intelligence MarketPractice - Applications of Artificial Intelligence














Artificial Intelligence Market, Exercise 1 (150 min.) [1]

1. Select a (market research) article on Artificial Intelligence. Note:The article to be treated should deal with the market and possibleindustrial technologies of Artificial Intelligence. The article shouldnot focus on individual technologies and systems. (A selection ofpossible articles can be found on the following slide).

2. Take a critical look at the article. Write a short presentationcontaining the main findings and information of the article as wellas a short abstract.






Selection of possible articles:

• “Reshaping Business with Artificial Intelligence -Closing the Gap Between Ambition and Action” [Group, 2017];

• “Future in the balance - How countries are pursuing an AIadvantage” [Deloitte, 2018];

• “Understanding Machines: Explainable AI” [Accenture, 2018];

• “Artificial Intelligence in Europe - Germany Outlook for 2019 andBeyond” [Ernst & Young Ltd., 2019];

• “10 theses about AI - A companies’ eye view of the future of AI”[Roland Berger Holding GmbH, 2018];

• “In-depth: Artificial Intelligence 2019 - Statista Digital MarketOutlook” [Statista Inc., 2019];






1. Discuss within a group of five students, where you see potentialsfor Artificial Intelligence technologies with regard to their use inprivate life, professional life and science.

2. In addition to the potentials, consider the opportunities, dangersand risks.






1. Discuss within a group of five students, which influence ArtificialIntelligence technologies currently have on your private life.Consider primarily the technologies, but also socio-politicalaspects.

2. Describe where Artificial Intelligence meets you in your privatelife, which technologies you currently use and which technologiesyou expect in the short, medium and long term.






1. Discuss within a group of five students, where you encounterArtificial Intelligence in science. Describe whether you havealready participated in a scientific work in this environment.

2. Estimate which sciences are concerned with artificial intelligenceissues and where you see the greatest scientific challenges withregard to Artificial Intelligence.



Logic Programming

Logic Programming (Basics)



Logic Programming



• Knowledge of what logic programming means.

• Knowledge about the basics in Prolog Programming.

• Knowledge to implement basic software in Prolog.



Logic Programming

Basics of Logical Programming

Logic Programming (Basics)Basics of Logical Programming



Logic Programming


Discussion: Basics of Logical Programming [1]


• Which programming languages do you know?

• Which programming paradigms do you know?

• Why there exist different programming paradigms?

• Do you know the most-widely used programming language?



Logic Programming


Recent Programming Languages [1]

Meanwhile, there is a variety of existing programming paradigms andlanguages. Each language differs from another language and has itsreason why it exists and more or less specific purpose.

According to the TIOBE Programming Community Index(shortened: TIOBE Index)65 – an index to demonstrate the proportionof use of programming languages – mainly object-oriented languagesare in use. An excerpt from the TIOBE Index shows this (see table 11and table 13).



Logic Programming



Table 11: TIOBE Index (July 2019) for the evaluation of programming languages withregard to their current significance (source: www.tiobe.com).

Programming Language Rating July 2019 (in %)Java 15.058C 14.211Python 9.260C++ 6.705C# 4.365Visual Basic .NET 4.208JavaScript 2.304PHP 2.167SQL 1.977Objectiv-C 1.686



Logic Programming



Table 12: Comparison development TIOBE Index for the evaluation of programminglanguages over recent years (source: www.tiobe.com).

Programming Language # July 2019 # July 2018 # 2009Java 1 1 1C 2 2 2Python 3 4 5C++ 4 3 3C# 5 6 6Visual Basic .NET 6 5 -JavaScript 7 8 8PHP 8 7 4SQL 9 9 -Objectiv-C 0 10 32



Logic Programming


Comparison between Programming Paradigms [1]

The most important feature of previously mentioned programminglanguages - but also in a general context - is the underlyingprogramming paradigm, i.e. how a programming language solves arequirement (see Figure 32, and Table ??).

Programming Paradigms

ImperativeProgramming

Object-orientedProgramming

ProceduraleProgramming

DeclarativeProgramming

LogicProgramming

FunctionaleProgramming

Figure 32: Classification of programming paradigms [Schukat-Talamazzini, 2009].



Logic Programming



Table 13: Programming languages and its corresponding programming paradigm.

Programming Language(s) Programming ParadigmPython, Erlang, R Functional ProgrammingProlog, Datalog Logic Programming

Fortran, Cobol, Algol, Pascal Procedurale ProgrammingC, C++, C#, Java Object-oriented Programming

In addition to the programming paradigms shown above, there arealso hybrid forms. Consequently, there exist languages that could beclassified into different paradigms, known as Multipardigm.Multiparadigm languages are for example, PHP, JavaScript, VisualBasic .NET, etc.



Logic Programming



The concepts behind the more differentiable paradigms are as shownbelow. As stated in the Figure above, two main directions ofprogramming paradigms exist:

• Declarative Programming languages are putting the probleminto the foreground, not the solution. The “What?” is concerned.Its sub-paradigms are functional programming, and logicprogramming.

• Imperative Programming languages put the solution in theforeground on the basis of a problem. It is asked for the How?.Its sub-paradigms are procedural programming, andobject-oriented programming.



Logic Programming



For the two main directions of programming paradigms, thesub-paradigms can be described as follows:

• Functional Programming is a declarative programming(sub)-paradigm, and its languages are based on mathematicprocedures.

• Logic Programming is a declarative programming(sub)-paradigm„ and its languages are based on logic (first-order,propositional),

• Procedurale Programming is an imperative programming(sub)-paradigm, is dividing the problem into differentsub-problems.

• Object-oriented Programming is an imperative programming(sub)-paradigm, and being based on modularization.



Logic Programming

Introduction to Prolog

Logic Programming (Basics)Introduction to Prolog



Logic Programming


Discussion: Introduction to Prolog [1]


• Do you know the programming language Prolog?

• Do you know how Prolog is working?

• In which area do you think Prolog is used?



Logic Programming


Prolog as Programming Language [1]

The term Prolog stands for Programming Logic. Prolog wasinvented by the computer scientist Alain Colerauer in 1972.

Prolog is a Logic Programming language and belongs – asfunctional programming languages (e.g. Python) – to the DeclarativeProgramming languages [Springer Link, 21] (see Figure 32). Logic isthe study of argumentation, which is made by the formation of chainsof linguistic units, which are related to each other [Dalen, 2013]. Thelogic of statements and predicates play an important role here. InProlog, the programming instructions (coding) is done using hornclauses.



Logic Programming


Prolog as Programming Language [2]

Prolog is mainly used in Artificial Intelligence, especially in thedevelopment of so-called expert systems or knowledge-basedsystems. Prolog does not focus on the solution, but on the descriptionof the problem. Based on the problem, Prolog searches for thesolution itself until it can be clearly stated if a solution exists or not. Itcontinues the search as long as possible, named Backtracking. Forthe result of the search, Prolog knows two possible states. Eachexecution of a Prolog program always (!) results always in one of thistwo possible states, as long as no other error is done:

• True means that a problem could have been found.

• False means that a problem could not have been found.



Logic Programming


Available Prolog Implementations [1]

The most well-known techniques used for implementing Prolog are(see Table 14):

• The open-source interpreter named SWI-Prolog66].

• The open-source interpreter named GNU-Prolog67].



Logic Programming


Available Prolog Implementations [2]

Table 14: Overview and comparison of Prolog interpreters.

Interpreter Licence Operating Systems OnlineSWI-Prolog Open Source Windows, Mac OS, Linux YesGNU-Prolog Open Source Windows, Mac OS, Linux No

66https://www.swi-prolog.org/67https://www.swi-prolog.org/



Logic Programming

Concepts of Prolog

Logic Programming (Basics)Concepts of Prolog



Logic Programming

Concepts of Prolog

Discussion: Concepts of Prolog [1]


• Do you know the concepts of Prolog?

• Do you know how to program in Prolog?

• Do you know what a knowledge base is?

• Do you know what backtracking is?

• Do you know what is meant by the term modus ponens?



Logic Programming

Concepts of Prolog

Example: Prolog Knowledge Base [1]

A program in Prolog consists of a so-called Knowledge Base. If aproblem is to be solved by means of Prolog, it is therefore said that aknowledge base is written - in contrast to other programminglanguages in which a program is written.

A knowledge base is ultimately a series of Predicates which arewritten by means of (horn) clauses. Each predicate consists of aFunctor, usually the name of the predicate and a set of argumentswritten in parentheses, e.g. woman(mia). The short form of apredicate uses the functor and the number of arguments written aftera slash. Related to our example the short form would be: woman/1.Since variables in Prolog are written in capitals, it is important to eitherstart atoms with a literal or put them in quotation marks.



Logic Programming

Concepts of Prolog


In Prolog there are three types of predicates [Angermann et al., 2017]:

• Facts represent predicates in the form of horn clauses, where nofurther predicates are used within rules. Facts are used toexplain things that are true for a particular situation of interest.

• Rules indicate information that, under certain conditions, appliesto the situation of interest. Prolog works on the principle ofModus Ponens, i.e. several rules can lead to one final rule.

• Queries may be true or false. If you want to get a problem solvedby Prolog, the agent (user) will ask the knowledge base to do so.Queries can be facts and rules.



Logic Programming

Concepts of Prolog


As can be seen from above the core knowledge is provided by thefacts, and the actual relationships between data (i.e. the knowledge)comes by the amount, complexity and semantics of the implementedrules. Each rule consists of two main parts:

• Body is the part including the facts and instructions.

• Tail is the part to perform the rule.

The body and the tail are separated by a binding symbol (“:-”), this canbe red as If-Sybmol, or just as If.[c,allowframebreaks] If you want to create a knowledge base aboutfamily members, and the relationships inside a family, that could lookas follows:



Logic Programming

Concepts of Prolog


• There exists a fact person/2, as given in Equation 14):

person/2 = person(A ,B), (14)

where A is a name, and B a gender.

• There exists a fact person/3, as given in Equation 15:

person/3 = person(A ,D,E), (15)

where D is the mothers’ name, and E the fathers’ name.

• There exists a rule child/3, as given in Equation 16:

child/3 = child(A ,B ,C). (16)



Logic Programming

Concepts of Prolog


Transferred into Prolog, the code looks as presnted below (seeListings 1, 2, 3.

Listing 1: person/1

person(bernd,male).person(erica,female).person(steve,male).person(britney,female).



Logic Programming

Concepts of Prolog


Listing 2: person/3

person(steve,erica,bernd).person(erica,erica,bernd).

Listing 3: child/3

child(A,B,C):-person(A,B,C),person(B,female),person(C,male).



Logic Programming

Concepts of Prolog


Based on the knowledge base (the included query), we can nowevaluate (query), for example:

• If B is mother of A.

• If C is father of A.

• If B has kid A.

• If C has kid A.

• If B and C have kid A.



Logic Programming

Concepts of Prolog


Of course, we can also evaluate (query) knowledge based on thefacts, if for example:

• If A is a male.

• If A is a female.

• If A has a name.

• If A has a mother.

• If A has a father.

• If A has a mother, and a father.



Logic Programming

Concepts of Prolog


A question is performed to the Prolog interpreter by putting a promptsymbol (“?-”) in front of the query, and by putting a dot (“.”) behind thequery. The former states that a query is performed. The latter statesthat the query ends here. Another possible query could look forexample like siblings/2 (see Listing 4).

Listing 4: child/3

?- siblings(A1,A2) :-child(A1,B,C),child(A2,B,C).



Logic Programming

Built-In Predicates

Logic Programming (Basics)Built-In Predicates



Logic Programming

Built-In Predicates

Discussion: Built-In Predicates [1]


• Do you know what is meant by built-in predicates?

• Do you know built-in predicates?



Logic Programming

Built-In Predicates

Introduction to Built-In Predicates [1]

Prolog has so-called Built-In Predicates. These are predefinedprocedures that can be used. Especially in more complex projects thissimplifies the programming process enormously.

Prolog has a large number of predefined predicates. Some of them –especially to learn the basic concept of Prolog are shown on thefollowing slides68:

68https://www.swi-prolog.org/pldoc/man?section=builtin


https://www.swi-prolog.org/pldoc/man?section=builtin


Logic Programming

Built-In Predicates

Data In-/Output Predicates [1]

consult/1 loads the prolog file to be executed;

include/1 loads the contents of a file to prolog;



Logic Programming

Built-In Predicates

Verification Predicates [1]

var/1 True if the term considered is a free variable;

nonvar/1 True if the term in question is not a free variable;

integer/1 True if the term in question is an integer;

float/1 True if the term under consideration is a floating point number;

number/1 True if the term in question is a floating point number or awhole point number;

string/1 True if the term in question is a string;

= /2 True if the union is successful;

== /2 True, if term A is equivalent to term B;



Logic Programming

Built-In Predicates

Controll Predicates, Meta-Call Predicates [1]

false/0 Wrong;

true/0 True;

!/0 Cut, i.e. the execution stops here;

not/1 True if the target cannot be proven;



Logic Programming

Built-In Predicates

Dynamic Predicates [1]

asserta/1 Adds a clause to the knowledge base.

retract/1 Removes a clause from the knowledge base.



Logic Programming

Built-In Predicates

Primitive Charcters, Write and Read Predicates [1]

nl/0 Produces a new line.

write/1 Writes the current term to the console.

read/1 Reads a term from the console.



Logic Programming

Built-In Predicates

Analysing & Constructing Atoms Predicates [1]

atomic_list_concat/3 Connects two atoms to a list.

atomic_length/2 Returns the length of an atom.



Logic Programming

Built-In Predicates

Character Properties Predicates [1]

downcase_atom2 Returns an atom in literals.

upcase_atom/2 Returns an atom in capitals.

atom_concat/3 Connects two atoms to a third atom.



Logic Programming

Built-In Predicates

Artithmetic Predicates [1]

> True if the expression to the left is greater than the expression tothe right.

< True if the expression to the left is smaller than the expression tothe right.

=< True if the left expression is equal to or less than the rightexpression.

== Equivalence.

is/1 True if the number is equal to the expression.



Logic Programming

Built-In Predicates

Artithmetic Functions Predicates [1]

− Addition.

+ Subtraction.

∗ Multiplication.

/ Division.

mod/2 Results the modulo operation.

abs/1 Results the absolute value.

max/2 Results the maximum value of a selection.

min/2 Results the minimum value of a selection.

random/1 Results a random number.

sqrt Results the Square of a value.

log/1 Results the natural logarithm of a value.



Logic Programming

Built-In Predicates

List Processing Predicates, Loop Predicates [1]

is_list/1 True if the argument is a list.

length/2 Results the length of a list.

sort/4 Sorts arguments in a list.

findall/3 Finds all solutions for a true statement.

setof/3 Results in a sorted list of arguments without duplicates.

forall/2 Processes a rule for all true statements.



Logic Programming

Some Prolog Examples

Logic Programming (Basics)Some Prolog Examples



Logic Programming


Discussion: Some Prolog Examples [1]


• Do you know Prolog applications?

• You have an idea for a Prolog application?



Logic Programming


Personalized Directories [1]

TaxoPublish: Towards a solution to automaticallypersonalize taxonomies in e-catalogs[Angermann and Ramzan, 2016].

Abstract:Taxonomies are utilized in e-catalogs to facilitate customers navigating through a marketplace with

the help of hierarchically structured concepts. However, when entering the e-catalog, each customer

is shown the identical taxonomy regardless their individual requirements. Customers are distracted

when navigating to preferred concepts as those are siblings of not required concepts. Provided

progress in dynamic taxonomies, catalog segmentation, and personalized directories lacks in a fully

automatic support for modifying the taxonomy according to the user’s requirements. The existing

works need an explicit user-query, are missing information about the domain, or require the

modification through the provider. In this paper, TaxoPublish expert system based on logic

programming is presented. The proposed system predicts the customers requirements for

automatically modifying the taxonomy in B2B context. With TaxoPublish, retailers can now provide



Logic Programming


Personalized Directories [2]

personalization in the form of personalized e-catalogs without any human effort, and without missing

any information about the domain. TaxoPublish is using knowledge provided through a Customer

Relationship Management system for predicting customers preferences, and knowledge of a

Product Information Management system for performing taxonomic operations based on two novel

types of taxonomic concepts. Through the usage of logic programming and the cross-platform

database model, TaxoPublish can be applied as expert system over distributed and heterogeneous

data warehouse architectures across various domains. The comprehensive experiments on two

public and one private database show that TaxoPublish expert system is capable of fully-automatic

taxonomy modification with an accuracy similar to the expert manual modifications.



Logic Programming


Natural Language Processing [1]

Taxo-Semantics: Assessing similarity between multi-wordexpressions for extending e-catalogs[Angermann et al., 2017].

Abstract:Taxonomies, also named directories, are utilized in e-catalogs to classify goods in a hierarchical

manner with the help of concepts. If there is a need to create new concepts when modifying the

taxonomy, the semantic similarity between the provided concepts has to be assessed properly.

Existing semantic similarity assessment techniques lack in a comprehensive support for

e-commerce, as those are not supporting multi-word expressions, multilingualism, the import/export

to relational databases, and supervised user-involvement. This paper proposes Taxo-Semantics, a

decision support system that is based on the progress in taxonomy matching to match each

expression against various sources of background knowledge. The similarity assessment is based

on providing three different matching strategies: a lexical-based strategy named

Taxo-Semantics-Label, the strategy Taxo-Semantics-Bk, which is using different sources of



Logic Programming


Natural Language Processing [2]

background knowledge, and the strategy Taxo-Semantics-User that is providing user-involvement.

The proposed system includes a translating service to analyze non-English concepts with the help

of the WordNet lexicon, can parse taxonomies of relational databases, supports user-involvement to

match single sequences with WordNet, and is capable to analyze each sequence as

(sub)-taxonomy. The three proposed matching strategies significantly outperformed existing

techniques. Taxo-Semantics-Label could improve the accuracy result by more than 7% as compared

to state-of-the-art lexical techniques. Taxo-Semantics-Bk could improve the accuracy compared to

structure-based techniques by more than 8%. And, Taxo-Semantics-User could additionally

increase the accuracy by on average 23%.



Logic Programming


Querying XML Documents [1]

Querying XML documents using Prolog engines: When isthis a good idea? [Santos et al., 2019].

Abstract:XML has become a universal standard for information exchange over the Web due to features such

as simple syntax and extensibility. Processing queries over these documents has been the focus of

several research groups. In fact, there is broad literature in efficient XML query processing which

explore indexes, fragmentation techniques, etc. However, for answering complex queries, existing

approaches mainly analyze information that is explicitly defined in the XML document. A few work

investigate the use of Prolog to increase the query possibilities, allowing inference over the data

content. This can cause a significant increase in the query possibilities and expressive power,

allowing access to non-obvious information. However, this requires translating the XML documents

into Prolog facts. But for regular queries (which do not require inference), is this a good alternative?

What kind of queries could benefit from the Prolog translation? Can we always use Prolog engines

to execute XML queries in an efficient way? There are many questions involved in adopting an



Logic Programming


Querying XML Documents [2]

alternative approach to run XML queries. In this work, we investigate this matter by translating XML

queries into Prolog queries and comparing the query processing times using Prolog and native XML

engines. Our work contributes by providing a set of heuristics that helps users to decide when to use

Prolog engines to process a given XML query. In summary, our results show that queries that

search elements by a key value or by its position (simple search) are more efficient when run in

Prolog than in native XML engines. Also, queries over large datasets, or that searches for substrings

perform better when run by native XML engines.



Logic Programming


Ontology Rules [1]

Domain-specific languages in Prolog for declarativeexpert knowledge in rules and ontologies[Seipel et al., 2018].

Abstract:Declarative if–then rules have proven very useful in many applications of expert systems. They can

be managed in deductive databases and evaluated using the well-known forward-chaining

approach. For domain-experts, however, the syntax of rules becomes complicated quickly, and

already many different knowledge representation formalisms exist. Expert knowledge is often

acquired in story form using interviews. In this paper, we discuss its representation by defining

domain-specific languages (Dsls) for declarative expert rules. They can be embedded in Prolog

systems in internal Dsls using term expansion and as external Dsls using definite clause grammars

and quasi-quotations – for more sophisticated syntaxes. Based on the declarative rules and the

integration with the Prolog-based deductive database system DDbase, multiple rules acquired in

practical case studies can be combined, compared, graphically analysed by domain-experts, and



Logic Programming


Ontology Rules [2]

evaluated, resulting in an extensible system for expert knowledge. As a result, the actual modeling

Dsl becomes executable; the declarative forward-chaining evaluation of deductive databases can be

understood by the domain experts. Our Dsl for rules can be further improved by integrating

ontologies and rule annotations.



Logic Programming


OWL Query Language [1]

A Prolog-based Query Language for OWL[Almendros-Jiménez, 2011].

Abstract:In this paper we investigate how to use logic programming (in particular, Prolog) as query language

against OWL resources. Our query language will be able to retrieve data and meta-data about a

given OWL based ontology. With this aim, firstly, we study how to define a query language based on

a fragment of Description Logic, then we show how to encode the defined query language into

Prolog by means of logic rules and finally, we identify Prolog goals which correspond to queries.



Logic Programming


Object-Oriented Databases [1]

On using Prolog to implement object-oriented databases[Paton et al., 1993].

Abstract:This paper outlines the use of Prolog for implementing object-oriented databases (OODBs), to

indicate both the benefits and costs associated with Prolog as an implementation platform. The

different roles which Prolog can play in the implementation of an OODB are illustrated by reference

to example systems which, although they use Prolog as an implementation language, have

significantly different architectures. These architectures are compared and assessed, both in terms

of the functionality provided to users, and performance.



Logic Programming


Subject Classification [1]

Classifications of subjects with the language PROLOG[Buzzi, 1989].

Abstract:The logical language PROLOG is used for the definition and characterization of groups of subjects.

The groups are firstly defined by sets of variables with comparable scales. Secondly, the single

members of the groups are characterized by logically structured combinations of variables which do

not necessarily have comparable scales. The performance of the characterizations is estimated by

determining the rates sensitivity and specificity. The new classification method is applied in a

follow-up study including the assessment of the activity of 76 healthy subjects during two controlled

experiments. The classification with PROLOG is then compared with the methods of logistic

regression and with discriminant analysis. The comparisons demonstrate that, under similar

conditions, the results of a classification with PROLOG parallel the results of statistically based

classification procedures. In addition, PROLOG permits characterizations of single subjects based

on variables from different scientific disciplines.



Logic Programming


Knowledge Base Modularization [1]

A modularization approach for PROLOG knowledge bases[Moily and Murray, 1993].

Abstract:One of the most critical disadvantages of knowledge based systems is their slow execution speed.

Knowledge based systems run about ten to one hundred times slower than traditional (procedural)

information systems. This has generally limited their application environments and constrained the

development of hybrid systems requiring both traditional and knowledge based approaches. This

paper presents a framework for modularizing PROLOG knowledge bases such that the inferencing

process to execute a goal is localized to an independent module consisting of only a subset of the

knowledge base. Note that the motivation for modularization in a knowledge based system is quite

different from the motivations for modularization in a traditional system. The presented

modularization approach is expected to contribute to the development of more efficient knowledge

based systems and more effective hybrid systems requiring both knowledge based and traditional

approaches.



Logic Programming

Practice - Logic Programming (Basics)

Logic Programming (Basics)Practice - Logic Programming (Basics)



Logic Programming









Logic Programming


Logic Programming (Basics), Exercise 1 (90 min.) [1]

1. Download and install a Prolog interpreter. Alternatively navigateto an online Prolog interpreter.

2. Design a taxonomy to structure the different academic rolesworking at an university.

3. Implement the taxonomy above using the different types ofpredicates provided by Prolog.

4. Your program should be capable to query the different roles, aswell as possible relationships and common features.



Logic Programming




2. Implement the product taxonomy of the online-shop by Amazon(use an excerpt of the German site).


4. Implement rules that compare different concepts of bothtaxonomies using various Name Similarity measures.

5. Evaluate the different measures by implementing rules thatmeasure the Balanced Accuracy of the techniques.



Logic Programming




2. Implement the product taxonomy of the online-shop by Amazon(use an excerpt of the German site).


4. Implement a rule that compares different concepts of the bothtaxonomies using the Levenshtein Distance measure.

5. Evaluate the different measures by implementing rules thatmeasure the Balanced Accuracy of the techniques.



References

References



References

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Colmerauer, A. and Roussel, P. (1992). The birth of prolog.

Dalen, D. (2013). Logic and Structure. Springer,Heidelberg/Berlin, Germany.



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