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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... successors in the search graph or nontip nodes of the search tree. The procedure orders the nodes on OPEN in step 8 so that the “best” of these is selected for expansion in step 4. This ordering can be based on a variety of heuristic ...
... successors of n in the search graph, G; in this case, a change might be in order to the parentage in T of the successors of n in G. Because G is finite, the process of propagating the costs of the new paths downward to the successors of ...
... successors of a node at once. It is possible to modify the algorithm so that a node is selected for expansion and successors are generated one at a time [see, for example, Michie and Ross (1970)]. The modified algorithm does not put a ...
... successor at a time. Usually, the backtracking implementation is preferred to the depth-first version of GRAPHSEARCH because backtracking is simpler to implement and involves less storage. (Backtracking strategies save only one path to ...
... successors are added to OPEN. For finite graphs, we ultimately run out of new successors, and thus, unless the algorithm terminates successfully in step 5 by finding a goal node, it will terminate in step 3 after eventually depleting ...
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 |