Abdul Rahim Ahmad

MITM 613Intelligent System

Chapter 0: Introduction

Contents Introduction Objectives Outcomes Chapters Plan Assessment References Conclusion and Expectations

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Introduction This course emphasises on the methods and

techniques that can be used to develop intelligent systems. knowledge-based techniques

expert and rule-based system object-oriented and frame-based systems intelligent agents.

computational intelligence or Machine Learning techniques

neural networks and its similar tools genetic algorithms Fuzzy logic

a hybrid of both. Abdul Rahim Ahmad

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Objectives To provide understanding of intelligent

systems and the various methods and tools in implementing Intelligent Systems.

To demonstrate the implementation of individual methods within the scope of Intelligent systems

To compare the pros and cons of each method of developing Intelligent Systems.

To develop the ability to implement a particular Intelligent system of choiceAbdul

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OutcomesAt the end of the course, you should be able

to: Explain the various methods of

implementing Intelligent systems Describe the issues involved in each

method of implementing an Intelligent System.

Describe the tools that can be used. Develop a particular intelligent system of

choice in a class project environment.Abdul Rahim Ahmad

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Main text Adrian A. Hopgood, Intelligent Systems for

Engineers and Scientists, 2nd Edition, CRC Publication (2000).

http://www.adrianhopgood.com/

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Chapters from Hopgood Chapter one: Introduction Chapter two: Rule-based systems Chapter three: Dealing with uncertainty Chapter four: Object-oriented systems Chapter five: Intelligent agents Chapter six: Symbolic learning Chapter seven: Optimization algorithms Chapter eight: Neural networks Chapter nine: Hybrid systems Chapter ten: Tools and languages Chapter eleven: Systems for interpretation and diagnosis Chapter twelve: Systems for design and selection Chapter thirteen: Systems for planning Chapter fourteen: Systems for control Chapter fifteen: Concluding remarks

Specifically on Genetic

Algorithm Additional

Chapter – Support Vector

Machine

Includes Fuzzy Logic

Chapter one: Introduction1.1 Intelligent systems1.2 Knowledge-based systems1.3 The knowledge base1.4 Deduction, abduction, and induction1.5 The inference engine1.6 Declarative and procedural programming1.7 Expert systems1.8 Knowledge acquisition1.9 Search1.10 Computational intelligence1.11 Integration with other software

Chapter two: Rule-based systems2.1 Rules and facts2.2 A rule-based system for boiler control2.3 Rule examination and rule firing2.4 Maintaining consistency2.5 The closed-world assumption2.6 Use of variables within rules2.7 Forward-chaining (a data-driven strategy)

2.7.1 Single and multiple instantiation of variables2.7.2 Rete algorithm

2.8 Conflict resolution2.8.1 First come, first served2.8.2 Priority values2.8.3 Metarules

2.9 Backward-chaining (a goal-driven strategy)2.9.1 The backward-chaining mechanism2.9.2 Implementation of backward-chaining2.9.3 Variations of backward-chaining

2.10 A hybrid strategy2.11 Explanation facilities

Chapter three: Dealing with uncertainty

3.1 Sources of uncertainty3.2 Bayesian updating

3.2.1 Representing uncertainty by probability

3.2.2 Direct application of Bayes’ theorem

3.2.3 Likelihood ratios3.2.4 Using the likelihood ratios3.2.5 Dealing with uncertain

evidence3.2.6 Combining evidence3.2.7 Combining Bayesian rules

with production rules3.2.8 A worked example of

Bayesian updating3.2.9 Discussion of the worked

example3.2.10 Advantages and

disadvantages of Bayesian updating

3.3 Certainty theory3.3.1 Introduction3.3.2 Making uncertain

hypotheses3.3.3 Logical combinations of

evidence3.3.4 A worked example of

certainty theory3.3.5 Discussion of the worked

example3.3.6 Relating certainty factors to

probabilities3.4 Possibility theory: fuzzy sets

and fuzzy logic3.4.1 Crisp sets and fuzzy sets3.4.2 Fuzzy rules3.4.3 Defuzzification

3.5 Other techniques3.5.1 Dempster–Shafer theory of

evidence3.5.2 Inferno

Chapter four: Object-oriented systems

Skipped

Chapter five: Intelligent agents5.1 Characteristics of an intelligent agent5.2 Agents and objects5.3 Agent architectures

5.3.1 Logic-based architectures5.3.2 Emergent behavior architectures5.3.3 Knowledge-level architectures5.3.4 Layered architectures

5.4 Multiagent systems5.4.1 Benefits of a multiagent system5.4.2 Building a multiagent system5.4.3 Communication between agents

Chapter six: Symbolic learning

Skipped

Chapter seven: Optimization algorithms

7.1 Optimization7.2 The search space7.3 Searching the search

space7.4 Hill-climbing and

gradient descent algorithms7.4.1 Hill-climbing7.4.2 Steepest gradient

descent or ascent7.4.3 Gradient-proportional

descent7.4.4 Conjugate gradient

descent or ascent7.5 Simulated annealing

7.6 Genetic algorithms 7.6.1 The basic GA 7.6.2 Selection 7.6.3 Gray code 7.6.4 Variable length

chromosomes 7.6.5 Building block

hypothesis 7.6.6 Selecting GA

parameters 7.6.7 Monitoring evolution 7.6.8 Lamarckian

inheritance 7.6.9 Finding multiple

optima 7.6.10 Genetic

programming

Chapter eight: Neural networks8.1 Introduction8.2 Neural network applications

8.2.1 Nonlinear estimation8.2.2 Classification8.2.3 Clustering8.2.4 Content-addressable memory

8.3 Nodes and interconnections8.4 Single and multilayer perceptrons

8.4.1 Network topology8.4.2 Perceptrons as classifiers8.4.3 Training a perceptron8.4.4 Hierarchical perceptrons8.4.5 Some practical considerations

8.5 The Hopfield network8.6 MAXNET8.7 The Hamming network8.8 Adaptive Resonance Theory (ART) networks8.9 Kohonen self-organizing networks8.10 Radial basis function networks

Chapter nine: Hybrid systems9.1 Convergence of techniques9.2 Blackboard systems9.3 Genetic-fuzzy systems9.4 Neuro-fuzzy systems9.5 Genetic-neural systems9.6 Clarifying and verifying neural networks9.7 Learning classifier systems

Chapter ten: Tools and languages 10.1 A range of intelligent systems tools 10.2 Expert system shells 10.3 Toolkits and libraries 10.4 Artificial intelligence languages

10.4.1 Lists 10.4.2 Other data types 10.4.3 Programming environments

10.5 Lisp 10.5.1 Background 10.5.2 Lisp functions 10.5.3 A worked example

10.6 Prolog 10.6.1 Background 10.6.2 A worked example 10.6.3 Backtracking in Prolog

10.7 Comparison of AI languages

Assessment Assignments (3 x 5) 15% Projects (best of 2 x 15) 15% Mid. Semester Examination 30% Final Examination 40%

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All References Adrian A. Hopgood, Intelligent Systems for

Engineers and Scientists, 2nd Edition, CRC Publication (2000).

Vojislav Kecman, Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Complex Adaptive Systems), MIT Press 2001

Artificial Intelligence, Elain Rich, Kevin Knight, Shivashanker Nair, McGraw Hill, 2009

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Conclusion/Expectations Able to explain fundamental concepts. Able to implement selected methods. Appreciation for using intelligent methods

in other field.

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