Artificial Intelligence

Series
Addison-Wesley
Author
Michael Negnevitsky  
Publisher
Addison-Wesley
Cover
Softcover
Edition
3
Language
English
Total pages
480
Pub.-date
May 2011
ISBN13
9781408225745
ISBN
1408225743
Related Titles



Description

Artificial Intelligence is one of the most rapidly evolving subjects within the computing/engineering curriculum, with an emphasis on creating practical applications from hybrid techniques. Despite this, the traditional textbooks continue to expect mathematical and programming expertise beyond the scope of current undergraduates and focus on areas not relevant to many of today's courses. Negnevitsky shows students how to build intelligent systems drawing on techniques from knowledge-based systems, neural networks, fuzzy systems, evolutionary computation and now also data mining.

The principles behind these techniques are explained without resorting to complex mathematics, showing how the various techniques are implemented, when they are useful and when they are not. No particular programming language is assumed and the book does not tie itself to any of the software tools available. However, available tools and their uses will be described and program examples will be given in MATLAB. The lack of assumed prior knowledge makes this book ideal for any introductory courses in artificial intelligence or intelligent systems design, while the contemporary coverage means more advanced students will benefit by discovering the latest state-of-the-art techniques.

The 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

 

Features

  • No mathematical or programming prerequisites. 
  • Linked coverage of all the latest artificial intelligence topics.
  • Question and answer format.
  • Accompanying website including student projects, accompanying software tools, software demonstrations, PowerPoint slides and solutions to exercises.

New to this Edition

  • The main objective of the book remains the same as in the first edition – to provide the reader with practical understanding of the field of computer intelligence. It is intended as an introductory text suitable for a one-semester course, and assumes the students have only limited knowledge of calculus and little or no programming experience.

     

    In terms of the coverage, this edition introduces a new chapter on data mining and demonstrates several new applications of intelligent tools for solving complex real-world problems. The major changes are as follows:

     

    ·         In the new chapter, ‘Data mining and knowledge discovery’, we introduce data mining as an integral part of knowledge discovery in large databases. We consider the main techniques and tools for turning data into knowledge, including statistical methods, data visualisation tools, Structured Query Language, decision trees and market basket analysis. We also present several case studies on data mining applications.

    ·         In Chapter 9, we add a new case study on clustering with a self-organising neural network.

     

    Finally, we have expanded the book’s references and bibliographies, and updated the list of AI tools and vendors in the appendix.

Table of Contents

 

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                                                                                        

 

Back Cover

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.

 

Author

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.

Reader Review(s)

“This book covers many areas related to my module. I would be happy to recommend this book to my students. I believe my students would be able to follow this book without any difficulty. Book chapters are very well organised and this will help me to pick and choose the subjects related to this module.” Dr Ahmad Lotfi, Nottingham Trent University, UK


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