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. |
From inside the book
Results 1-5 of 32
Page 20
... condition forms the basis for the termination condition of the production system . The control strategy repeatedly applies rules to state descriptions until a description of a goal state is produced . It also keeps track of the rules ...
... condition forms the basis for the termination condition of the production system . The control strategy repeatedly applies rules to state descriptions until a description of a goal state is produced . It also keeps track of the rules ...
Page 21
... termination condition , do : 3 begin 4 5 6 select some rule , R , in the set of rules that can be applied to DATA DATA result of applying R to DATA end 1.1.3 . CONTROL The above procedure is nondeterministic because we have not yet ...
... termination condition , do : 3 begin 4 5 6 select some rule , R , in the set of rules that can be applied to DATA DATA result of applying R to DATA end 1.1.3 . CONTROL The above procedure is nondeterministic because we have not yet ...
Page 23
... termination condition . Applying hill - climbing to the 8 - puzzle we might use , as a function of the state description , the negative of the number of tiles " out of place , " as compared to the goal state description . For example ...
... termination condition . Applying hill - climbing to the 8 - puzzle we might use , as a function of the state description , the negative of the number of tiles " out of place , " as compared to the goal state description . For example ...
Page 27
... termination condition . In Figure 1.4 , we show all applicable rules being applied to every state description . This sort of indecision on the part of the control system is usually grossly inefficient because the resulting tree grows ...
... termination condition . In Figure 1.4 , we show all applicable rules being applied to every state description . This sort of indecision on the part of the control system is usually grossly inefficient because the resulting tree grows ...
Page 30
... termination condition . Notice that we can use. Fig . 1.6 A search tree for the traveling salesman problem . Fig . 1.10 An AND / OR tree for a. 30 PRODUCTION SYSTEMS AND AI.
... termination condition . Notice that we can use. Fig . 1.6 A search tree for the traveling salesman problem . Fig . 1.10 An AND / OR tree for a. 30 PRODUCTION SYSTEMS AND AI.
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 |
Other editions - View all
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