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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The processes required to represent problems initially and to improve given
representations are still poorly understood . It seems that desirable shifts in a
problem ' s representation depend on experience gained in attempts to solve it in
An interesting property of the AND / OR graph representation of a wff is that the
set of clauses into which that wff could have been converted can be read out as
the set of solution graphs ( terminating in leaf nodes ) of the AND / OR graph .
CHAPTER 9 STRUCTURED OBJECT REPRESENTATIONS As we discussed in
chapter 4 , there are many ways to represent a body of knowledge in the
predicate calculus . The appropriateness of a representation depends on the
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