Principles of Artificial IntelligenceA 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 Intelligenceevolved 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. |
From inside the book
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... semantic networks stems from many sources . In cognitive psychology , Quillian ( 1968 ) , Anderson and Bower ( 1973 ) ... network " languages " have now been proposed that have the full expressive power of predicate calculus . Shapiro's ...
... network and represent the STRIPS rule pickup ( x ) as a production rule for changing networks . Explain how the rule ... semantic network . Illustrate by an example . Can you think of any other useful arc predicates ? 9.3 Represent the ...
... semantic networks . Artificial Intelligence , 7 ( 2 ) , pp . 163-198 . Schubert , L. K. , Goebel , R. G. , and Cercone , N. J. 1979. The structure and organization of a semantic net for comprehension and inference . In AN , pp . 121-175 ...
Contents
PROLOGUE | 1 |
PRODUCTION SYSTEMS AND AI | 17 |
SEARCH STRATEGIES FOR | 53 |
Copyright | |
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