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<

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