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 5
... components of reasoning . Many informal tasks , including medical diagnosis and information retrieval , can be formalized as theorem - proving problems . For these reasons , theorem proving is an extremely important topic in the study ...
... components of reasoning . Many informal tasks , including medical diagnosis and information retrieval , can be formalized as theorem - proving problems . For these reasons , theorem proving is an extremely important topic in the study ...
Page 7
... primitive picture components such as line segments , simple curves , corners , etc. These , in turn , are processed to infer information about the three - dimensional character of the 7 SOME APPLICATIONS OF ARTIFICIAL INTELLIGENCE.
... primitive picture components such as line segments , simple curves , corners , etc. These , in turn , are processed to infer information about the three - dimensional character of the 7 SOME APPLICATIONS OF ARTIFICIAL INTELLIGENCE.
Page 8
... components of a description . These hypotheses are then tested by detectors that are specialized to the component descriptions . The outcomes of these tests , in turn , are used to develop better hypotheses , etc. This hypothesize - and ...
... components of a description . These hypotheses are then tested by detectors that are specialized to the component descriptions . The outcomes of these tests , in turn , are used to develop better hypotheses , etc. This hypothesize - and ...
Page 14
... components , one of which is a rule - based system for synthesizing programs from descriptions of abstract algorithms [ Barstow ( 1979 ) ] . Rich and Shrobe ( 1979 ) describe a programmer's apprentice system for assisting a human ...
... components , one of which is a rule - based system for synthesizing programs from descriptions of abstract algorithms [ Barstow ( 1979 ) ] . Rich and Shrobe ( 1979 ) describe a programmer's apprentice system for assisting a human ...
Page 17
... components of data , operations , and control . That is , if these systems are described at an appropriate level , one can often identify a central entity that might be called a global database that is manipulated by certain well ...
... components of data , operations , and control . That is , if these systems are described at an appropriate level , one can often identify a central entity that might be called a global database that is manipulated by certain well ...
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