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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It turns out that the order ( F1 , F2 , R1 , R2 ) for rule and fact matching and the
order in which we have written the antecedents of rules Rl and ... There might be
a match against F1 , so we check to see if cons ( 1 , cons ( 2 , NIL ) unifies with
If such a match can be found , we say that the precondition of the F - rule matches
the facts . We call the unifying composition , the match substitution . For a given F
- rule and state description , there may be many match substitutions .
definition must be something like : Two objects match if and only if the predicate
calculus formula associated with one of them unifies with the predicate calculus
formula associated with the other . We are interested in a somewhat weaker ...
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