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Artificial Intelligence

A Guide to Intelligent Systems

Paperback Engels 2011 9781408225745
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Samenvatting

Artificial Intelligence is often perceived as being a highly complicated, even frightening, subject in Computer Science. This view is compounded by books in this area being crowded with complex matrix algebra and differential equations – until now. This book, evolving from lectures given to students with little knowledge of calculus, assumes no prior programming experience and demonstrates that most of the underlying ideas in intelligent systems are, in reality, simple and straightforward. The main attraction of the author's approach is in his deliberate de-emphasising of the maths – just enough to give a valid treatment of the subject. This is what makes the underlying ideas in AI so much easier to understand. No wonder that this book has already been adopted by more than 250 universities around the world and translated into many languages.
Are you looking for a genuinely lucid, introductory text for a course in AI or Intelligent Systems Design? Perhaps you’re a non-computer science professional looking for a self-study guide to the state-of-the art in knowledge-based systems? Either way, you can’t afford to ignore this book.
Covers:

-Rule-based expert systems
-Fuzzy expert systems
-Frame-based expert systems
-Artificial neural networks
-Evolutionary computation
-Hybrid intelligent systems
-Knowledge engineering
-Data mining

New to this edition:

-New chapter on data mining and knowledge discovery
-New section on clustering with a self-organising neural network
-Four new case studies
-Completely updated to incorporate the latest developments in this fast-paced field.

Dr Michael Negnevitsky is a Professor in Electrical Engineering and Computer Science at the University of Tasmania, Australia. The book has developed from his lectures to undergraduates. Educated as an electrical engineer, Dr Negnevitsky’s many interests include artificial intelligence and soft computing. His research involves the development and application of intelligent systems in electrical engineering, process control and environmental engineering. He has authored and co-authored over 300 research publications including numerous journal articles, four patents for inventions and two books.

Specificaties

ISBN13:9781408225745
Taal:Engels
Bindwijze:paperback
Verschijningsdatum:19-5-2011
Hoofdrubriek:IT-management / ICT
ISSN:

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Inhoudsopgave



Contents





Preface xii

New to this edition xiii

Overview of the book xiv

Acknowledgements xvii



1 Introduction to knowledge-based intelligent systems 1



1.1 Intelligent machines, or what machines can do 1

1.2 The history of artificial intelligence, or from the ‘Dark Ages’
to knowledge-based systems 4

1.3 Summary 17
Questions for review 21
References 22



2 Rule-based expert systems 25



2.1 Introduction, or what is knowledge? 25

2.2 Rules as a knowledge representation technique 26

2.3 The main players in the expert system development team 28

2.4 Structure of a rule-based expert system 30

2.5 Fundamental characteristics of an expert system 33

2.6 Forward chaining and backward chaining inference
techniques 35

2.7 MEDIA ADVISOR: a demonstration rule-based expert system 41

2.8 Conflict resolution 47

2.9 Advantages and disadvantages of rule-based expert systems 50

2.10 Summary 51
Questions for review 53
References 54



3 Uncertainty management in rule-based expert systems 55



3.1 Introduction, or what is uncertainty? 55

3.2 Basic probability theory 57

3.3 Bayesian reasoning 61

3.4 FORECAST: Bayesian accumulation of evidence 65

3.5 Bias of the Bayesian method 72

3.6 Certainty factors theory and evidential reasoning 74

3.7 FORECAST: an application of certainty factors 80

3.8 Comparison of Bayesian reasoning and certainty factors 82

3.9 Summary 83
Questions for review 85
References 85



4 Fuzzy expert systems 87



4.1 Introduction, or what is fuzzy thinking? 87

4.2 Fuzzy sets 89

4.3 Linguistic variables and hedges 94

4.4 Operations of fuzzy sets 97

4.5 Fuzzy rules 103

4.6 Fuzzy inference 106

4.7 Building a fuzzy expert system 114

4.8 Summary 125
Questions for review 126
References 127
Bibliography 127



5 Frame-based expert systems 131



5.1 Introduction, or what is a frame? 131

5.2 Frames as a knowledge representation technique 133

5.3 Inference in frame-based experts 138

5.4 Methods and demons 142

5.5 Interaction of frames and rules 146

5.6 Buy Smart: a frame-based expert system 149

5.7 Summary 161
Questions for review 163
References 163
Bibliography 164



6 Artificial neural networks 165



6.1 Introduction, or how the brain works 165

6.2 The neuron as a simple computing element 168

6.3 The perceptron 170

6.4 Multilayer neural networks 175

6.5 Accelerated learning in multilayer neural networks 185

6.6 The Hopfield network 188

6.7 Bidirectional associative memories 196

6.8 Self-organising neural networks 200

6.9 Summary 212
Questions for review 215
References 216



7 Evolutionary computation 219



7.1 Introduction, or can evolution be intelligent? 219

7.2 Simulation of natural evolution 219

7.3 Genetic algorithms 222

7.4 Why genetic algorithms work 232

7.5 Case study: maintenance scheduling with genetic
algorithms 235

7.6 Evolutionary strategies 242

7.7 Genetic programming 245

7.8 Summary 254
Questions for review 255
References 256
Bibliography 257



8 Hybrid intelligent systems 259



8.1 Introduction, or how to combine German mechanics
with Italian love 259

8.2 Neural expert systems 261

8.3 Neuro-fuzzy systems 268

8.4 ANFIS: Adaptive Neuro-Fuzy Inference System 277

8.5 Evolutionary neural networks 285

8.6 Fuzzy evolutionary systems 290

8.7 Summary 296
Questions for review 297
References 298



9 Knowledge engineering 301



9.1 Introduction, or what is knowledge engineering? 301

9.2 Will an expert system work for my problem? 308

9.3 Will a fuzzy expert system work for my problem? 317

9.4 Will a neural network work for my problem? 323

9.5 Will genetic algorithms work for my problem?

9.6 Will a hybrid intelligent system work for my problem?

9.7 Summary
Questions for review
References



10 Data mining and knowledge discovery



10.1 Introduction, or what is data mining?

10.2 Statistical methods and data visualisation

10.3 Principal components analysis

10.4 Relational databases and database queries

10.5 The data warehouse and multidimensional data analysis

10.6 Decision trees

10.7 Association rules and market basket analysis

10.8 Summary
Questions for review
References



Glossary

Appendix

Index


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