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 34
... F - rules . If , instead , we choose to employ problem goal descriptions as the global database , we shall say that ... rule or an applicable B - rule . 1.2 . SPECIALIZED PRODUCTION SYSTEMS 1.2.1 . COMMUTATIVE PRODUCTION SYSTEMS 34 ...
... F - rules . If , instead , we choose to employ problem goal descriptions as the global database , we shall say that ... rule or an applicable B - rule . 1.2 . SPECIALIZED PRODUCTION SYSTEMS 1.2.1 . COMMUTATIVE PRODUCTION SYSTEMS 34 ...
Page 44
... rule instantiation covers each alternative . The decomposition rule states that the integral of a sum can be replaced by the sum of the integrands . Another rule , called the factoring rule , allows us to replace the expression sk f ( x ) ...
... rule instantiation covers each alternative . The decomposition rule states that the integral of a sum can be replaced by the sum of the integrands . Another rule , called the factoring rule , allows us to replace the expression sk f ( x ) ...
Page 84
... f ( n2 ) ≥ g ( n1 ) + h ( n1 ) = f ( n , ) . Since this fact is true for ... rule is to enhance the chances that the first path discovered to a node will ... F. ) 2.4.6 . THE HEURISTIC POWER OF EVALUATION FUNCTIONS The selection ...
... f ( n2 ) ≥ g ( n1 ) + h ( n1 ) = f ( n , ) . Since this fact is true for ... rule is to enhance the chances that the first path discovered to a node will ... F. ) 2.4.6 . THE HEURISTIC POWER OF EVALUATION FUNCTIONS The selection ...
Page 97
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Page 110
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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