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. |
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... implication . The left - hand side of an implication is called the antecedent , and the right - hand side is called the consequent . If both the antecedent and the consequent are wffs , then the implication is a wff also . An ...
... implication are assumed to have universal quantification over the entire implication . Variables in the facts and rules are standardized apart so that no variable occurs in more than one rule and so that the rule variables are different ...
... implication : { EL ( x , DEPARTMENTS ) ^ EQ [ manager ( x ) , y ] } EQ [ works - in ( y ) , x ] might be represented by the network structure shown in Figure 9.26 . To use a network implication as a forward rule , the ANTE structure ...
Contents
PROLOGUE | 1 |
PRODUCTION Systems and AI | 17 |
SEARCH Strategies FOR | 53 |
Copyright | |
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