Abdul Rahim Ahmad

MITM 613Intelligent System

Chapter 0: Introduction

Contents

Introduction

Objectives

Outcomes

Chapters

Plan

Assessment

References

Conclusion and ExpectationsAbdul Rahim Ahmad

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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

Rahim Ahmad

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Outcomes

At 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.

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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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Abdul Rahim Ahmad

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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: Introduction

1.1 Intelligent systems

1.2 Knowledge-based systems

1.3 The knowledge base

1.4 Deduction, abduction, and induction

1.5 The inference engine

1.6 Declarative and procedural programming

1.7 Expert systems

1.8 Knowledge acquisition

1.9 Search

1.10 Computational intelligence

1.11 Integration with other software

Chapter two: Rule-based systems

2.1 Rules and facts

2.2 A rule-based system for boiler control

2.3 Rule examination and rule firing

2.4 Maintaining consistency

2.5 The closed-world assumption

2.6 Use of variables within rules

2.7 Forward-chaining (a data-driven strategy)

2.7.1 Single and multiple instantiation of variables

2.7.2 Rete algorithm

2.8 Conflict resolution

2.8.1 First come, first served

2.8.2 Priority values

2.8.3 Metarules

2.9 Backward-chaining (a goal-driven strategy)

2.9.1 The backward-chaining mechanism

2.9.2 Implementation of backward-chaining

2.9.3 Variations of backward-chaining

2.10 A hybrid strategy

2.11 Explanation facilities

Chapter three: Dealing with uncertainty

3.1 Sources of uncertainty

3.2 Bayesian updating

3.2.1 Representing uncertainty by probability

3.2.2 Direct application of Bayes’ theorem

3.2.3 Likelihood ratios

3.2.4 Using the likelihood ratios

3.2.5 Dealing with uncertain evidence

3.2.6 Combining evidence

3.2.7 Combining Bayesian rules with production rules

3.2.8 A worked example of Bayesian updating

3.2.9 Discussion of the worked example

3.2.10 Advantages and disadvantages of Bayesian updating

3.3 Certainty theory3.3.1 Introduction

3.3.2 Making uncertain hypotheses

3.3.3 Logical combinations of evidence

3.3.4 A worked example of certainty theory

3.3.5 Discussion of the worked example

3.3.6 Relating certainty factors to probabilities

3.4 Possibility theory: fuzzy sets and fuzzy logic3.4.1 Crisp sets and fuzzy sets

3.4.2 Fuzzy rules

3.4.3 Defuzzification

3.5 Other techniques3.5.1 Dempster–Shafer theory of

evidence

3.5.2 Inferno

Chapter four: Object-oriented systems

Skipped

Chapter five: Intelligent agents

5.1 Characteristics of an intelligent agent

5.2 Agents and objects

5.3 Agent architectures5.3.1 Logic-based architectures

5.3.2 Emergent behavior architectures

5.3.3 Knowledge-level architectures

5.3.4 Layered architectures

5.4 Multiagent systems5.4.1 Benefits of a multiagent system

5.4.2 Building a multiagent system

5.4.3 Communication between agents

Chapter six: Symbolic learning

Skipped

Chapter seven: Optimization algorithms

7.1 Optimization

7.2 The search space

7.3 Searching the search space

7.4 Hill-climbing and gradient descent algorithms

7.4.1 Hill-climbing

7.4.2 Steepest gradient descent or ascent

7.4.3 Gradient-proportional descent

7.4.4 Conjugate gradient descent or ascent

7.5 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 networks

8.1 Introduction

8.2 Neural network applications

8.2.1 Nonlinear estimation

8.2.2 Classification

8.2.3 Clustering

8.2.4 Content-addressable memory

8.3 Nodes and interconnections

8.4 Single and multilayer perceptrons

8.4.1 Network topology

8.4.2 Perceptrons as classifiers

8.4.3 Training a perceptron

8.4.4 Hierarchical perceptrons

8.4.5 Some practical considerations

8.5 The Hopfield network

8.6 MAXNET

8.7 The Hamming network

8.8 Adaptive Resonance Theory (ART) networks

8.9 Kohonen self-organizing networks

8.10 Radial basis function networks

Chapter nine: Hybrid systems

9.1 Convergence of techniques

9.2 Blackboard systems

9.3 Genetic-fuzzy systems

9.4 Neuro-fuzzy systems

9.5 Genetic-neural systems

9.6 Clarifying and verifying neural networks

9.7 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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