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
... nodes labeled by the results of rule applications . These successor nodes are called OR nodes because in order to ... tip nodes " correspond to databases that each satisfy the termination condition . We shall discuss strategies for ...
... nodes labeled by the results of rule applications . These successor nodes are called OR nodes because in order to ... tip nodes " correspond to databases that each satisfy the termination condition . We shall discuss strategies for ...
Page 63
... tip node . We say that the root node is of depth zero . The depth of any other node in the tree is defined to be the depth of its parent plus 1 . A sequence of nodes ... nodes is then the sum of the costs of all of the arcs connecting ...
... tip node . We say that the root node is of depth zero . The depth of any other node in the tree is defined to be the depth of its parent plus 1 . A sequence of nodes ... nodes is then the sum of the costs of all of the arcs connecting ...
Page 65
... tip nodes of the search tree , and the nodes on CLOSED are the nontip nodes . More precisely , at step 3 of the ... node ( except the root node ) of a tree 65 GRAPH - SEARCH STRATEGIES.
... tip nodes of the search tree , and the nodes on CLOSED are the nontip nodes . More precisely , at step 3 of the ... node ( except the root node ) of a tree 65 GRAPH - SEARCH STRATEGIES.
Page 101
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Page 115
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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