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
Results 1-3 of 89
... variables for the variables appearing in P [ x , f ( y ) , B ] . The last of the four instances shown above is called a ground instance , since none of the terms in the literal contains variables . . .... We can represent any ...
... VARIABLES We now describe forward production systems that deal with expres- sions containing variables . We have already mentioned that variables in facts and rules have implicit universal quantification . We assume that any existential ...
... variables , and these variables can be matched against anything when invoking a program . Since F - rule patterns are used only to match facts and B - rule patterns are used only to match goals , the use of ? -variables in both patterns ...
Contents
PROLOGUE | 1 |
PRODUCTION SYSTEMS AND AI | 17 |
SEARCH STRATEGIES FOR | 53 |
Copyright | |
10 other sections not shown