Negnevitsky shows students how to build intelligent systems drawing on techniques from knowledge-based systems, neural networks, fuzzy systems, evolutionary computation and now also intelligent agents. 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 are described, and program examples are given in Java. Includes the latest state-of-the-art techniques, particularly in intelligent agents and knowledge discovery. • 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.
1 Introduction to knowledge-based intelligent systems
2 Rule-based expert systems
3 Uncertainty management in rule-based expert systems
4 Fuzzy expert systems
5 Frame-based expert systems
6 Artificial neural networks
7 Evolutionary computation
8 Hybrid intelligent systems
9 Knowledge engineering
10 Data mining and knowledge discoveryDr 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 NegnevitskyÂ’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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