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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... Goal Node 19 :| :: ii : : i ::: |:: i|. Fig. 2.6A search tree produced by a depth-first search. Fig. 2.8A search tree using an evaluation function.
... goal node. In principle, these methods provide a solution to the path-finding problem, but they are often infeasible to use to control AI production systems because the search expands too many nodes before a path is found. Since there ...
... node and the goal set have been suggested; or in board games or puzzles, a configuration is often scored points on the basis of those features that it possesses that are thought to be related to its promise as a step toward the goal ...
... node n estimates the sum of the cost of the minimal cost path from the start nodes to node n plus the cost of a minimal cost path from node n to a goal node. That is, f(n) is an estimate of the. Fig. 2.8A search tree using an evaluation ...
... nodes having no path between them.) The cost of a minimal cost path from node n to some particular goal node, ti, is then given by k(n, ti). We let h"(n) be the minimum of all of the k (n, ti) over the entire set of goal nodes {t}. Thus ...
Contents
1 | |
17 | |
53 | |
CHAPTER 3 SEARCH STRATEGIES FOR DECOMPOSABLE PRODUCTION SYSTEMS | 99 |
CHAPTER 4 THE PREDICATE CALCULUS IN AI | 131 |
CHAPTER 5 RESOLUTION REFUTATION SYSTEMS | 161 |
CHAPTER 6 RULEBASED DEDUCTION SYSTEMS | 193 |
CHAPTER 7 BASIC PLANGENERATING SYSTEMS | 275 |
CHAPTER 8 ADVANCED PLANGENERATING SYSTEMS | 321 |
CHAPTER 9 STRUCTURED OBJECT REPRESENTATIONS | 361 |
PROSPECTUS | 417 |
BIBLIOGRAPHY | 429 |
AUTHOR INDEX | 467 |
SUBJECT INDEX | 471 |