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 13
... recursive functions and makes strong use of induction . Regular workshops are held on automatic deduction . An informal proceedings was issued for the Fourth Workshop [ see WAD in the Bibliography ] . 0.3.5 . ROBOTICS Much of the ...
... recursive functions and makes strong use of induction . Regular workshops are held on automatic deduction . An informal proceedings was issued for the Fourth Workshop [ see WAD in the Bibliography ] . 0.3.5 . ROBOTICS Much of the ...
Page 55
... recursive procedure captures the essence of the operation of a production system under backtracking control . This procedure , which we call BACKTRACK , takes a single argument , DATA , initially set equal to the global database of the ...
... recursive procedure captures the essence of the operation of a production system under backtracking control . This procedure , which we call BACKTRACK , takes a single argument , DATA , initially set equal to the global database of the ...
Page 56
... recursively on the new database . 9 if PATH = FAIL , go LOOP ; if the recursive call fails , try another rule . 10 return CONS ( R , PATH ) ; otherwise , pass the successful list of rules up , by adding R to the front of the list . We ...
... recursively on the new database . 9 if PATH = FAIL , go LOOP ; if the recursive call fails , try another rule . 10 return CONS ( R , PATH ) ; otherwise , pass the successful list of rules up , by adding R to the front of the list . We ...
Page 57
Nils J. Nilsson. Any recursive call fails when its depth exceeds this bound . Cycling can be more straightforwardly prevented by maintaining a list of the databases produced so far and by checking new ones to see that they do not match ...
Nils J. Nilsson. Any recursive call fails when its depth exceeds this bound . Cycling can be more straightforwardly prevented by maintaining a list of the databases produced so far and by checking new ones to see that they do not match ...
Page 58
... recursive procedure , the entire chain of databases must be an argument of the procedure . Again , practical implementations of AI backtracking production systems use various techniques to avoid the need for explicitly listing all of ...
... recursive procedure , the entire chain of databases must be an argument of the procedure . Again , practical implementations of AI backtracking production systems use various techniques to avoid the need for explicitly listing all of ...
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