Cover image for Artificial intelligence : a guide to intelligent systems
Title:
Artificial intelligence : a guide to intelligent systems
Author:
Negnevitsky, Michael.
Personal Author:
Edition:
First edition.
Publication Information:
Harlow : Addison-Wesley, [2002]

©2002
Physical Description:
xiv, 394 pages : illustrations ; 24 cm
Language:
English
ISBN:
9780201711592
Format :
Book

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Summary

Summary

Soft ComputingArtificial IntelligenceA Guide to Intelligent SystemsMichael NegnevitskyVirtually all the literature on artificial intelligence is expressed in the jargon of computer science, crowded with complex matrix algebra and differential equations. Unlike many other books on computer intelligence, this one demonstrates that most ideas behind intelligent systems are simple and straightforward. The book has evolved from lectures given to students with little knowledge of calculus, and the reader needs no prerequisites associated with knowledge of any programming language. The methods used in the book have been extensively tested through several courses given by the author.The book provides an introduction to the field of computer intelligence, covering rule-based expert systems, fuzzy expert systems, frame-based expert systems, artificial neural networks, evolutionary computation, hybrid intelligent systems, knowledge engineering, data mining.In a university setting the book can be used as an introductory course within computer science, information systems or engineering departments. The book is also suitable as a self-study guide for non-computer science professionals, giving a


Author Notes

Dr Michael Negnevitsky is a Senior Lecturer in Electrical Engineering and Computer Science at the University of Tasmania, Australia


Table of Contents

Prefacep. xi
Acknowledgementsp. xv
1 Introduction to knowledge-based intelligent systemsp. 1
1.1 Intelligent machines, or what machines can dop. 1
1.2 The history of artificial intelligence, or from the 'Dark Ages' to knowledge-based systemsp. 4
1.3 Summaryp. 17
Questions for reviewp. 21
Referencesp. 22
2 Rule-based expert systemsp. 25
2.1 Introduction, or what is knowledge?p. 25
2.2 Rules as a knowledge representation techniquep. 26
2.3 The main players in the expert system development teamp. 28
2.4 Structure of a rule-based expert systemp. 30
2.5 Fundamental characteristics of an expert systemp. 33
2.6 Forward chaining and backward chaining inference techniquesp. 35
2.7 THERMOSTAT: a demonstration rule-based expert systemp. 41
2.8 Conflict resolutionp. 46
2.9 Advantages and disadvantages of rule-based expert systemsp. 49
2.10 Summaryp. 51
Questions for reviewp. 53
Referencesp. 53
3 Uncertainty management in rule-based expert systemsp. 55
3.1 Introduction, or what is uncertainty?p. 55
3.2 Basic probability theoryp. 57
3.3 Bayesian reasoningp. 61
3.4 FORECAST: Bayesian accumulation of evidencep. 65
3.5 Bias of the Bayesian methodp. 72
3.6 Certainty factors theory and evidential reasoningp. 74
3.7 FORECAST: an application of certainty factorsp. 80
3.8 Comparison of Bayesian reasoning and certainty factorsp. 82
3.9 Summaryp. 83
Questions for reviewp. 85
Referencesp. 85
4 Fuzzy expert systemsp. 87
4.1 Introduction, or what is fuzzy thinking?p. 87
4.2 Fuzzy setsp. 89
4.3 Linguistic variables and hedgesp. 94
4.4 Operations of fuzzy setsp. 97
4.5 Fuzzy rulesp. 103
4.6 Fuzzy inferencep. 106
4.7 Building a fuzzy expert systemp. 114
4.8 Summaryp. 125
Questions for reviewp. 126
Referencesp. 127
Bibliographyp. 127
5 Frame-based expert systemsp. 129
5.1 Introduction, or what is a frame?p. 129
5.2 Frames as a knowledge representation techniquep. 131
5.3 Inheritance in frame-based systemsp. 136
5.4 Methods and demonsp. 140
5.5 Interaction of frames and rulesp. 144
5.6 Buy Smart: a frame-based expert systemp. 147
5.7 Summaryp. 159
Questions for reviewp. 161
Referencesp. 161
Bibliographyp. 162
6 Artificial neural networksp. 163
6.1 Introduction, or how the brain worksp. 163
6.2 The neuron as a simple computing elementp. 166
6.3 The perceptronp. 168
6.4 Multilayer neural networksp. 173
6.5 Accelerated learning in multilayer neural networksp. 183
6.6 The Hopfield networkp. 186
6.7 Bidirectional associative memoryp. 194
6.8 Self-organising neural networksp. 198
6.9 Summaryp. 210
Questions for reviewp. 213
Referencesp. 214
7 Evolutionary computationp. 217
7.1 Introduction, or can evolution be intelligent?p. 217
7.2 Simulation of natural evolutionp. 217
7.3 Genetic algorithmsp. 220
7.4 Why genetic algorithms workp. 230
7.5 Case study: maintenance scheduling with genetic algorithmsp. 233
7.6 Evolution strategiesp. 240
7.7 Genetic programmingp. 243
7.8 Summaryp. 252
Questions for reviewp. 253
Referencesp. 254
8 Hybrid intelligent systemsp. 257
8.1 Introduction, or how to combine German mechanics with Italian lovep. 257
8.2 Neural expert systemsp. 259
8.3 Neuro-fuzzy systemsp. 266
8.4 ANFIS: Adaptive Neuro-Fuzzy Inference Systemp. 275
8.5 Evolutionary neural networksp. 283
8.6 Fuzzy evolutionary systemsp. 288
8.7 Summaryp. 294
Questions for reviewp. 295
Referencesp. 296
9 Knowledge engineering and data miningp. 299
9.1 Introduction, or what is knowledge engineering?p. 299
9.2 Will an expert system work for my problem?p. 306
9.3 Will a fuzzy expert system work for my problem?p. 315
9.4 Will a neural network work for my problem?p. 321
9.5 Data mining and knowledge discoveryp. 330
9.6 Summaryp. 341
Questions for reviewp. 342
Referencesp. 343
Glossaryp. 345
Appendixp. 371
Indexp. 387