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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Thus , A , always expands at least as many nodes as does the more informed Ag
. We prove this result using induction on the depth of a node in the Ag search tree
at termination . First , we prove that if A , expands a node n having zero depth ...
That is , one should not go about attempting to prove that Sam , say , is an animal
by first setting up the subgoal of proving that he is a cat and , failing in that , trying
the other subgoals . The taxonomic hierarchy branches out too extensively in ...
21 A net for proving that Clyde is gray . Next , suppose we want to prove that
Clyde is warm - blooded when we know only that Clyde is an elephant . Again ,
we move up the taxonomic hierarchy to the delineation unit for MAMMALS where
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