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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QUANTIFICATION Sometimes an atomic formula , like P ( x ) , has value T ( with a
given interpretation for P ) no matter what assignment is given to the ... Here , the
formula being quantified is an implication , and x is the quantified variable .
... form of the goal wff . The underlined part of the answer statement is obviously
similar to the entire goal wff - with g ( x ) taking the place of the existentially
quantified variable x in the goal wff , and f ( g ( x ) ) taking the place of the
GOAL WFFS CONTAINING UNIVERSALLY QUANTIFIED VARIABLES A problem
arises when the goal wff contains universally quantified variables . These
universally quantified variables become existentially quantified in the negation of
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PRODUCTION SYSTEMS AND AI
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