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Artificial Intelligence: A Guide to Intelligent Systems (2nd - download pdf or read online

By Michael Negnevitsky

ISBN-10: 0321204662

ISBN-13: 9780321204660

Man made Intelligence is likely one of the such a lot swiftly evolving topics in the computing/engineering curriculum, with an emphasis on developing useful purposes from hybrid strategies. regardless of this, the normal textbooks proceed to anticipate mathematical and programming services past the scope of present undergraduates and concentrate on parts now not suitable to a lot of today's classes. Negnevitsky indicates scholars find out how to construct clever platforms drawing on strategies from knowledge-based structures, neural networks, fuzzy platforms, evolutionary computation and now additionally clever brokers. the foundations at the back of those concepts are defined with no resorting to advanced arithmetic, displaying how many of the thoughts are carried out, after they are priceless and once they should not. No specific programming language is believed and the booklet doesn't tie itself to any of the software program instruments on hand. besides the fact that, on hand instruments and their makes use of may be defined and software examples can be given in Java. the inability of assumed earlier wisdom makes this ebook excellent for any introductory classes in man made intelligence or clever structures layout, whereas the contempory assurance ability extra complicated scholars will gain by way of gaining knowledge of the most recent cutting-edge innovations.

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Extra info for Artificial Intelligence: A Guide to Intelligent Systems (2nd Edition)

Sample text

Conventional programs do not make mistakes – but programmers sometimes do. Unlike conventional programs, expert systems do not follow a prescribed sequence of steps. They permit inexact reasoning and can deal with incomplete, uncertain and fuzzy data. Can expert systems make mistakes? Even a brilliant expert is only a human and thus can make mistakes. This suggests that an expert system built to perform at a human expert level also should be allowed to make mistakes. But we still trust experts, although we do recognise that their judgements are sometimes wrong.

In a rule-based expert system, the knowledge is represented as a set of rules. Each rule specifies a relation, recommendation, directive, strategy or heuristic and has the IF (condition) THEN (action) structure. When the condition part of a rule is satisfied, the rule is said to fire and the action part is executed. The database includes a set of facts used to match against the IF (condition) parts of rules stored in the knowledge base. The inference engine carries out the reasoning whereby the expert system reaches a solution.

LonguestHiggins and D. Michie, eds, Science Research Council of Great Britain, London. McCarthy, J. (1958). Programs with common sense, Proceedings of the Symposium on Mechanisation of Thought Processes, vol. 1, London, pp. 77–84. S. and Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics, vol. 5, pp. 115–137. Medsker, L. and Leibowitz, J. (1994). Design and Development of Expert Systems and Neural Computing. Macmillan, New York. L.

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Artificial Intelligence: A Guide to Intelligent Systems (2nd Edition) by Michael Negnevitsky


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