We work with leading authors to develop the strongest educational materials in computer science, bringing cutting-edge thinking and best learning practice to a global market. Rule-based expert systems Fuzzy expert systems Frame-based expert systems Artificial neural networks Evolutionary computation Hybrid intelligent systems Knowledge engineering Data miningĪrtificial Intelligence A Guide to Intelligent Systems Second Edition He has authored and co-authored over 250 research publications including numerous journal articles, four patents for inventions and two books. His research involves the development and application of intelligent systems in electrical engineering, process control and environmental engineering. Educated as an electrical engineer, Dr Negnevitsky’s many interests include artificial intelligence and soft computing. Its material has also been extensively tested through short courses introduced at Otto-von-Guericke-Universität Magdeburg, Institut Elektroantriebstechnik, Magdeburg, Germany, Hiroshima University, Japan and Boston University and Rochester Institute of Technology, USA. The book has developed from lectures to undergraduates. 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 fieldĭr Michael Negnevitsky is a Professor in Electrical Engineering and Computer Science at the University of Tasmania, Australia.
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.
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. This view is compounded by books in this area being crowded with complex matrix algebra and differential equations – until now. Artificial Intelligence is often perceived as being a highly complicated, even frightening subject in Computer Science.
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