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 28
Page 6
... achieve a stated result is closely related to the task of proving that a given program achieves a stated result . The latter is called program verification . Many automatic programming systems produce a verification of the output ...
... achieve a stated result is closely related to the task of proving that a given program achieves a stated result . The latter is called program verification . Many automatic programming systems produce a verification of the output ...
Page 10
... achieve prescribed goals . These methods are illustrated by considering simple problems in robot planning and automatic program- ming . Chapter 7 introduces some of the more basic ideas , and chapter 8 elaborates on the subjects of ...
... achieve prescribed goals . These methods are illustrated by considering simple problems in robot planning and automatic program- ming . Chapter 7 introduces some of the more basic ideas , and chapter 8 elaborates on the subjects of ...
Page 20
... achieve any one of an explicit list of problem states . A further generalization is to specify some true / false condition on states to serve as a goal condition . Then the goal would be to achieve any state satisfying this condition ...
... achieve any one of an explicit list of problem states . A further generalization is to specify some true / false condition on states to serve as a goal condition . Then the goal would be to achieve any state satisfying this condition ...
Page 23
... achieve maximum increase in the value of this function by moving the blank up , so our production system selects the corresponding rule . In Figure 1.2 we show the sequence of states traversed by such a production system in solving this ...
... achieve maximum increase in the value of this function by moving the blank up , so our production system selects the corresponding rule . In Figure 1.2 we show the sequence of states traversed by such a production system in solving this ...
Page 34
... achieve this effect with production systems also . To do so , we must incorporate both state descriptions and goal descriptions into the global database . F - rules are applied to the state description part , while B - rules are applied ...
... achieve this effect with production systems also . To do so , we must incorporate both state descriptions and goal descriptions into the global database . F - rules are applied to the state description part , while B - rules are applied ...
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