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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Page 122
... ( Note that the savings in search effort would have been even greater if we were searching to greater depths ; for then none of the descendants of nodes B , C , and D would have to be generated either . ) It is important to observe that ...
... ( Note that the savings in search effort would have been even greater if we were searching to greater depths ; for then none of the descendants of nodes B , C , and D would have to be generated either . ) It is important to observe that ...
Page 169
... Note that the first level of Figure 5.4 is the same as the first level of Figure 5.2 . At subsequent levels , the linear - input form strategy does reduce the number of clauses produced . Again , the use of this strategy on our example ...
... Note that the first level of Figure 5.4 is the same as the first level of Figure 5.2 . At subsequent levels , the linear - input form strategy does reduce the number of clauses produced . Again , the use of this strategy on our example ...
Page 256
... Note that our CANCEL graph method treats conjunctively related goal nodes correctly . Each conjunct must be proved before the parent is proved . Disjunctively related fact nodes are treated in a similar manner . In order to use one ...
... Note that our CANCEL graph method treats conjunctively related goal nodes correctly . Each conjunct must be proved before the parent is proved . Disjunctively related fact nodes are treated in a similar manner . In order to use one ...
Contents
PROLOGUE | 1 |
PRODUCTION SYSTEMS AND AI | 17 |
SEARCH STRATEGIES FOR | 53 |
Copyright | |
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Common terms and phrases
8-puzzle achieve actions Adders AI production algorithm AND/OR graph applied Artificial Intelligence atomic formula backed-up value backtracking backward block breadth-first breadth-first search called chapter clause form CLEAR(C component contains control regime control strategy cost DCOMP Deleters delineation depth-first search described discussed disjunction domain element-of evaluation function example existentially quantified F-rule formula frame problem global database goal expression goal node goal stack goal wff graph-search HANDEMPTY heuristic HOLDING(A implication initial state description knowledge leaf nodes literal nodes logic negation node labeled ONTABLE(A optimal path pickup(A precondition predicate calculus problem-solving procedure production system proof prove recursive regress represent representation result robot problem rule applications search graph search tree selected semantic network sequence shown in Figure Skolem function solution graph solve SRI International stack(A STRIPS structure subgoal substitutions successors Suppose symbols termination condition theorem theorem-proving tip nodes universally quantified unstack(C,A variables WORKS-IN