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. |
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We let h"(n) be the minimum of all of the k (n, ti) over the entire set of goal nodes {
t}. Thus, h”(n) is the cost of the minimal cost path from n to a goal node, and any
path from node n to a goal node that achieves h”(n) is an optimal path from n to a
...
(This path is the lowest cost path from s to n found so far by the search algorithm.
... Let us say that a search algorithm is admissible if, for any graph, it always
terminates in an optimal path from s to a goal node whenever a path from s to a
goal ...
Next we would like to show that if a path from s to a goal node exists, A* will
terminate even for infinite graphs. ... (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.) ...
But the f* value of any node on an optimal path is equal to f*(s), the minimal cost,
and therefore f(n) < f*(s). Thus, we have: RESULT 2: At any time before A*
terminates, there exists on OPEN a node n' that is on an optimal path from s to a
goal ...
the least costly paths in G from nodes to the descendants of node n. In addition to
the burden of ... We now show that, given the monotone restriction, when A*
expands a node, it has found an optimal path to that node. Let n be any node ...
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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 |
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Bibliographic information
| Title | Principles of Artificial Intelligence |
| Author | Nils J. Nilsson |
| Edition | reprint |
| Publisher | Morgan Kaufmann, 2014 |
| ISBN | 1483295869, 9781483295862 |
| Length | 476 pages |
| Subjects | › Computers / General |
| Export Citation | BiBTeX EndNote RefMan |