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 41
Nils J. Nilsson. Rules can be applied to component databases . Nodes labeled by these component databases have ... node corresponding to a component database satisfying the termination condition ( in this case consisting of the ...
Nils J. Nilsson. Rules can be applied to component databases . Nodes labeled by these component databases have ... node corresponding to a component database satisfying the termination condition ( in this case consisting of the ...
Page 62
... nodes are labeled by databases , and the arcs are labeled by rules . If an arc is directed from node n1 to node n ;, then node n ; is said to be a successor of node n1 , and node n1 is said to be a parent of node n ;. In the graphs that ...
... nodes are labeled by databases , and the arcs are labeled by rules . If an arc is directed from node n1 to node n ;, then node n ; is said to be a successor of node n1 , and node n1 is said to be a parent of node n ;. In the graphs that ...
Page 66
... nodes labeled by the same database . Node repetitions , of course , lead to redundant successor computations . Hence , there is a tradeoff between the computational cost of testing for matching databases and the computational cost of ...
... nodes labeled by the same database . Node repetitions , of course , lead to redundant successor computations . Hence , there is a tradeoff between the computational cost of testing for matching databases and the computational cost of ...
Page 69
... node ; they do not save the entire record of the search as do depth - first graph - search strategies . ) The search ... labeled with their corresponding databases and are numbered in the order in which they are selected for ...
... node ; they do not save the entire record of the search as do depth - first graph - search strategies . ) The search ... labeled with their corresponding databases and are numbered in the order in which they are selected for ...
Page 99
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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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Common terms and phrases
8-puzzle achieve actions Adders 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 game tree 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 methods monotone restriction negation node labeled ONTABLE(A optimal path pickup(A precondition predicate calculus problem-solving procedure production rules production system proof prove recursive regress represent representation resolution refutation result robot problem rule applications search graph search tree semantic network sequence shown in Figure Skolem function solution graph solve stack(A STRIPS structure subgoal substitutions successors Suppose symbols termination condition theorem theorem-proving tip nodes unifying composition universally quantified