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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... algorithm in which the database just produced is illustrated by the array in Figure 2.2. (In fact, the BACKTRACK algorithm would produce precisely this array using the arbitrary rule ordering that we originally discussed.) The algorithm ...
... algorithms are thus of special interest to us. Before describing these algorithms, we first review some graph-theory terminology. A graph consists of a (not necessarily finite) set of nodes. Certain pairs of nodes are connected by arcs ...
... algorithms. The procedure generates an explicit graph, G, called the search graph and a subset, T, of G called the ... algorithm is preserved explicitly in G, a single distinguished path to any node is defined by T. Roughly speaking ...
... algorithm, and there is no need to change parents of the nodes in T. If the implicit graph being searched is not a tree, it is possible that some of the members of M have already been generated, that is, they may already be on OPEN or ...
... algorithm selects node 1 for expansion. (We assume unit arc costs.) When node 1 is expanded, its single successor, node 2, is generated. But node 2, with parent node 3 in the search tree, had previously been generated, and node 2 is ...
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 |