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 68
... first search and breadth - first search . The first type of uninformed search orders the nodes on OPEN in descending order of their depth in the ... SEARCH STRATEGIES FOR AI PRODUCTION SYSTEMS 2.3. Uninformed Graph-search Procedures.
... first search and breadth - first search . The first type of uninformed search orders the nodes on OPEN in descending order of their depth in the ... SEARCH STRATEGIES FOR AI PRODUCTION SYSTEMS 2.3. Uninformed Graph-search Procedures.
Page 69
... search process generated only one successor at a time . Usually , the backtracking implementation is preferred to the depth - first ... breadth - first because expansion of nodes in the search tree proceeds along " contours " of equal ...
... search process generated only one successor at a time . Usually , the backtracking implementation is preferred to the depth - first ... breadth - first because expansion of nodes in the search tree proceeds along " contours " of equal ...
Page 71
... 4 754 27 283283 123 123 74 714 61565 . 84 784 765 65 Goal Node Fig . 2.7 A search tree produced by a breadth - first search . UNINFORMED GRAPH - SEARCH PROCEDURES 2.4 . HEURISTIC GRAPH - SEARCH PROCEDURES The uninformed search.
... 4 754 27 283283 123 123 74 714 61565 . 84 784 765 65 Goal Node Fig . 2.7 A search tree produced by a breadth - first search . UNINFORMED GRAPH - SEARCH PROCEDURES 2.4 . HEURISTIC GRAPH - SEARCH PROCEDURES The uninformed search.
Page 72
Nils J. Nilsson. 2.4 . HEURISTIC GRAPH - SEARCH PROCEDURES The uninformed search methods , whether breadth - first or depth - first , are exhaustive methods for finding paths to a goal node . In principle , these methods provide a ...
Nils J. Nilsson. 2.4 . HEURISTIC GRAPH - SEARCH PROCEDURES The uninformed search methods , whether breadth - first or depth - first , are exhaustive methods for finding paths to a goal node . In principle , these methods provide a ...
Page 74
... search methods , although the use of the evaluation function has resulted in substantially fewer nodes being expanded . ( If we simply use the evaluation function f ( n ) = d ( n ) , we get the breadth - first search process . ) The ...
... search methods , although the use of the evaluation function has resulted in substantially fewer nodes being expanded . ( If we simply use the evaluation function f ( n ) = d ( n ) , we get the breadth - first search process . ) The ...
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