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 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 will be described and program examples will be given in Java. The lack of assumed prior knowledge makes this book ideal for any introductory courses in artificial intelligence or intelligent systems design, while the contempory coverage means more advanced students will benefit by discovering the latest state-of-the-art techniques.
[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-Universit™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.
