Artificial Intelligence : A Guide to Intelligent Systems

by Negnevitsky, Michael

• ISBN13: 9780321204660
• ISBN10: 0321204662
• Edition: 2nd
• Format: Hardcover
• Copyright: 2004-11-02
• Publisher: Addison-Wesley

Summary

[Shelving Category] Artificial Intelligence/Soft Computing 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. Are you looking for a genuinely lucid, introductory text for a course in A.I 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 demonstration rule-based system, MEDIA ADVISOR New section on genetic algorithms 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 lectures to undergraduates. Its material has also been extensively tested through short courses introduced at Otto-von-Guericke-Universittrade;t Magdeburg, Institut Elektroantriebstechnik, Magdeburg, Germany, Hiroshima University, Japan and Boston University and Rochester Institute of Technology, USA 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 250 research publications including numerous journal articles, four patents for inventions and two books.

Author Biography

Michael Negnevitsky is a Professor in Electrical Engineering and Computer Science at the University of Tasmania, Australia.

Table of Contents

Preface	xi
Preface to the second edition	xv
Acknowledgements	xvii
Introduction to knowledge-based intelligent systems	1	(24)
Intelligent machines, or what machines can do	1	(3)
The history of artificial intelligence, or from the `Dark Ages' to knowledge-based systems	4	(13)
Summary	17	(8)
Questions for review	21	(1)
References	22	(3)
Rule-based expert systems	25	(30)
Introduction, or what is knowledge?	25	(1)
Rules as a knowledge representation technique	26	(2)
The main players in the expert system development team	28	(2)
Structure of a rule-based expert system	30	(3)
Fundamental characteristics of an expert system	33	(2)
Forward chaining and backward chaining inference techniques	35	(6)
MEDIA ADVISOR: a demonstration rule-based expert system	41	(6)
Conflict resolution	47	(3)
Advantages and disadvantages of rule-based expert systems	50	(1)
Summary	51	(4)
Questions for review	53	(1)
References	54	(1)
Uncertainty management in rule-based expert systems	55	(32)
Introduction, or what is uncertainty?	55	(2)
Basic probability theory	57	(4)
Bayesian reasoning	61	(4)
Forecast: Bayesian accumulation of evidence	65	(7)
Bias of the Bayesian method	72	(2)
Certainty factors theory and evidential reasoning	74	(6)
Forecast: an application of certainty factors	80	(2)
Comparison of Bayesian reasoning and certainty factors	82	(1)
Summary	83	(4)
Questions for review	85	(1)
References	85	(2)
Fuzzy expert systems	87	(44)
Introduction, or what is fuzzy thinking?	87	(2)
Fuzzy sets	89	(5)
Linguistic variables and hedges	94	(3)
Operations of fuzzy sets	97	(6)
Fuzzy rules	103	(3)
Fuzzy inference	106	(8)
Building a fuzzy expert system	114	(11)
Summary	125	(6)
Questions for review	126	(1)
References	127	(1)
Bibliography	127	(4)
Frame-based expert systems	131	(34)
Introduction, or what is a frame?	131	(2)
Frames as a knowledge representation technique	133	(5)
Inheritance in frame-based systems	138	(4)
Methods and demons	142	(4)
Interaction of frames and rules	146	(3)
Buy Smart: a frame-based expert system	149	(12)
Summary	161	(4)
Questions for review	163	(1)
References	163	(1)
Bibliography	164	(1)
Artificial neural networks	165	(54)
Introduction, or how the brain works	165	(3)
The neuron as a simple computing element	168	(2)
The perceptron	170	(5)
Multilayer neural networks	175	(10)
Accelerated learning in multilayer neural networks	185	(3)
The Hopfield network	188	(8)
Bidirectional associative memory	196	(4)
Self-organising neural networks	200	(12)
Summary	212	(7)
Questions for review	215	(1)
References	216	(3)
Evolutionary computation	219	(40)
Introduction, or can evolution be intelligent?	219	(1)
Simulation of natural evolution	219	(3)
Genetic algorithms	222	(10)
Why genetic algorithms work	232	(3)
Case study: maintenance scheduling with genetic algorithms	235	(7)
Evolution strategies	242	(3)
Genetic programming	245	(9)
Summary	254	(5)
Questions for review	255	(1)
References	256	(1)
Bibliography	257	(2)
Hybrid intelligent systems	259	(42)
Introduction, or how to combine German mechanics with Italian love	259	(2)
Neural expert systems	261	(7)
Neuro-fuzzy systems	268	(9)
ANFIS: Adaptive Neuro-Fuzzy Inference System	277	(8)
Evolutionary neural networks	285	(5)
Fuzzy evolutionary systems	290	(6)
Summary	296	(5)
Questions for review	297	(1)
References	298	(3)
Knowledge engineering and data mining	301	(64)
Introduction, or what is knowledge engineering?	301	(7)
Will an expert system work for my problem?	308	(9)
Will a fuzzy expert system work for my problem?	317	(6)
Will a neural network work for my problem?	323	(13)
Will genetic algorithms work for my problem?	336	(3)
Will a hybrid intelligent system work for my problem?	339	(10)
Data mining and knowledge discovery	349	(12)
Summary	361	(4)
Questions for review	362	(1)
References	363	(2)
Glossary	365	(26)
Appendix	391	(16)
Index	407