Principles of Artificial Intelligence
A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, "Principles of Artificial Intelligence" describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used.
"Principles of Artificial Intelligence"evolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.
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We assume that were MAX to choose among tip nodes , he would choose that
node having the largest evaluation . Therefore , the ( MAX node ) parent of MIN
tip nodes is assigned a backed - up value equal to the maximum of the
know whether A or B is true , we might attempt first to prove the goal by assuming
that A is true and then attempt to prove the goal assuming B is true . ... We
assume that any existential variables in facts and rules have been Skolemized .
With these facts and rules , a simple backward production system can answer a
variety of questions . For these examples , we assume that the control strategy
guides the generation of the AND / OR graph by pursuing a depth - first search for
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PRODUCTION SYSTEMS AND AI
SEARCH STRATEGIES FOR
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