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 3
... example , suppose the facts are the personnel records of a large corporation . Example items in such a database might be representations for such facts as " Joe Smith works in the Purchasing Department , " " Joe Smith was hired on ...
... example , suppose the facts are the personnel records of a large corporation . Example items in such a database might be representations for such facts as " Joe Smith works in the Purchasing Department , " " Joe Smith was hired on ...
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... example is the traveling salesman's problem , where the problem is to find a minimum distance tour , starting at one of several cities , visiting each city precisely once , and returning to the starting city . The problem generalizes to ...
... example is the traveling salesman's problem , where the problem is to find a minimum distance tour , starting at one of several cities , visiting each city precisely once , and returning to the starting city . The problem generalizes to ...
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... example , the number of cities would be a measure of the size of a traveling salesman problem . ) Thus , problem difficulty may grow linearly , polynomially , or exponentially , for example , with problem size . The time taken by the ...
... example , the number of cities would be a measure of the size of a traveling salesman problem . ) Thus , problem difficulty may grow linearly , polynomially , or exponentially , for example , with problem size . The time taken by the ...
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... example , perhaps a detector could be built that could test a scene to see if it belonged to the category " A hill with a tree on top with cattle grazing . " But why should this detector be selected instead of the countless others that ...
... example , perhaps a detector could be built that could test a scene to see if it belonged to the category " A hill with a tree on top with cattle grazing . " But why should this detector be selected instead of the countless others that ...
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... example , in the 8 - puzzle , we might want to achieve any tile configuration for which the sum of the numbers labeling the tiles in the first row is 6. In our language of states , moves , and goals , a solution to a problem is a ...
... example , in the 8 - puzzle , we might want to achieve any tile configuration for which the sum of the numbers labeling the tiles in the first row is 6. In our language of states , moves , and goals , a solution to a problem is a ...
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