Artificial Intelligence: A Guide to Intelligent Systems 2nd edition [Kõva köide]

Negnevitsky (electrical engineering and computer science, U. of Tasmania) takes a straightforward, jargonless approach to artificial intelligence, believing that the concepts behind it are remarkably simple and easy to follow. He tackles these concepts in nine sections that work more like essays than textbook chapters, although each contains questions for review. He covers the basics of knowledge-based intelligent systems, the structure of various expert systems, uncertainty management, artificial neural networks, evolutionary computation, and hybrid intelligent systems. Along the way he describes the history of the science, the people who developed it, Bayesian reasoning and certainty factors, fuzzy logic, machine learning, neural expert systems, and data mining. For this new edition Negnevitsky adds case studies, information on a new rule-based system, and a section on genetic algorithms. Annotation ©2004 Book News, Inc., Portland, OR (booknews.com)

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.