Artificial Intelligence: A Guide to Intelligent Systems

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9780201711592: Artificial Intelligence: A Guide to Intelligent Systems
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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

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Virtually all the literature on artificial intelligence is expressed in the jargon of commuter 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,
  • artificail 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 access to the state of the art in knowledge-based systems and computational intelligence. Everyone who faces challenging problems and cannot solve them using traditional approaches can benefit

About the 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.

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