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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... cost of a minimal cost path from the start node to a goal node constrained to go through node n. We use the function f to order the nodes on OPEN in step 8 of GRAPHSEARCH. By convention, the nodes on OPEN are ordered in increasing order ...
... cost paths; whereas, the use of an evaluation function that overestimates the promise of all nodes (such as 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 ...
... cost of a minimal cost path between two arbitrary nodes n, and n, . (The function k is undefined for 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 ...
Nils J. Nilsson. summing the arc costs encountered while tracing the pointers from n to s. (This path is the lowest cost path from s to n found so far by the search algorithm. The value of g(n) for certain nodes may decrease if the ...
... cost of each arc in the graph is at least some small positive numbere, g”(n) > d”(n)e. (Recall that g”(n) is the cost of the optimal path from s to n, and that g(n) is the cost of the path in the search tree from s to node n.) Clearly ...
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 |