Artificial Intelligence, 3/e
Winston
- 出版商: Addison Wesley
- 出版日期: 1992-05-10
- 售價: $920
- 貴賓價: 9.8 折 $901
- 語言: 英文
- 頁數: 737
- 裝訂: Paperback
- ISBN: 0201533774
- ISBN-13: 9780201533774
-
相關分類:
人工智慧
已絕版
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商品描述
Description
This is the all-time bestselling introduction to artificial intelligence. The third edition retains the best features of the earlier works, including superior readability, currency, and excellence in the selection of examples. Winston emphasizes how artificial intelligence can be viewed from an engineering or a scientific point of view. The new edition offers comprehensive coverage of more material, and many of the ideas presented are enhanced with a variety of side pieces, including application examples such as the Westinghouse nuclear fuel plant optimizer. 
Table Of Contents
I. REPRESENTATIONS AND METHODS.
1. The Intelligent Computer.
2. Semantic Nets and Description Matching.
3. Generate and Test, Means-End Analysis, and Problem Reduction.
4. Nets and Basic Search ¥ Nets and Optimal Search.
5. Nets and Optimal Search.
6. Trees and Adversarial Search.
7. Rules and Rule Chaining.
8. Rules, Substrates, and Cognitive Modeling.
9. Frames and Inheritance.
10. Frames and Commonsense.
11. Numeric Constraints and Propagation.
12. Symbolic Constraints and Propagation.
13. Logic and Resolution Proof.
14. Backtracking and Truth Maintenance.
15. Planning.
The Field and the Book.
This Book Has Three Parts.
What Artificial Intelligence Can Do.
Criteria for Success.
Summary
Background.
This Book Has Three Parts.
What Artificial Intelligence Can Do.
Criteria for Success.
Summary
Background.
2. Semantic Nets and Description Matching.
Semantic Nets.
The Describe-and-Match Method.
The Describe-and-Match Method and Analogy Problems.
The Describe-and-Match Method and Recognition of Abstractions.
Problem Solving and Understanding Knowledge.
Summary.
Background.
The Describe-and-Match Method.
The Describe-and-Match Method and Analogy Problems.
The Describe-and-Match Method and Recognition of Abstractions.
Problem Solving and Understanding Knowledge.
Summary.
Background.
3. Generate and Test, Means-End Analysis, and Problem Reduction.
The Generate-and-Test Method.
The Means-Ends Analysis Method.
The Problem-Reduction Method.
Summary.
Background.
The Means-Ends Analysis Method.
The Problem-Reduction Method.
Summary.
Background.
4. Nets and Basic Search ¥ Nets and Optimal Search.
Blind Methods.
Heuristically Informed Methods.
Summary.
Background.
Heuristically Informed Methods.
Summary.
Background.
5. Nets and Optimal Search.
The Best PathRedundant Paths.
Summary.
Background.
Summary.
Background.
6. Trees and Adversarial Search.
Algorithmic Methods.
Heuristic Methods.
Summary.
Background.
Heuristic Methods.
Summary.
Background.
7. Rules and Rule Chaining.
Rule-Based Deduction Systems.
Rule-Based Reaction Systems.
Procedures for Forward and Backward Chaining.
Summary.
Background.
Rule-Based Reaction Systems.
Procedures for Forward and Backward Chaining.
Summary.
Background.
8. Rules, Substrates, and Cognitive Modeling.
Rule-Based Systems Viewed as Substrate.
Rule-Based Systems Viewed as Models for Human Problem Solving.
Summary.
Background.
Rule-Based Systems Viewed as Models for Human Problem Solving.
Summary.
Background.
9. Frames and Inheritance.
Frames, Individuals, and Inheritance.
Demon ProceduresFrames, Events, and Inheritance.
Summary.
Background.
Demon ProceduresFrames, Events, and Inheritance.
Summary.
Background.
10. Frames and Commonsense.
Thematic-role Frames.
Examples Using Take Illustrate How Constraints Interact.
Expansion into Primitive Actions.
Summary.
Background.
Examples Using Take Illustrate How Constraints Interact.
Expansion into Primitive Actions.
Summary.
Background.
11. Numeric Constraints and Propagation.
Propagation of Numbers Through Numeric Constraint Nets.
Propagation of Probability Bounds Through Opinion Nets.
Propagation of Surface Altitudes Through Arrays.
Summary.
Background.
Propagation of Probability Bounds Through Opinion Nets.
Propagation of Surface Altitudes Through Arrays.
Summary.
Background.
12. Symbolic Constraints and Propagation.
Propagation of Line Labels through Drawing Junctions.
Propagation of Time-Interval Relations.
Five Points of Methodology.
Summary.
Background.
Propagation of Time-Interval Relations.
Five Points of Methodology.
Summary.
Background.
13. Logic and Resolution Proof.
Rules of Inference.
Resolution Proofs.
Summary.
Background.
Resolution Proofs.
Summary.
Background.
14. Backtracking and Truth Maintenance.
Chronological and Dependency-Directed Backtracking.
Proof by Constraint Propagation.
Summary.
Background.
Proof by Constraint Propagation.
Summary.
Background.
15. Planning.
Planning Using If-Add-Delete Operators.
Planning Using Situation Variables.
Summary.
Background.
Planning Using Situation Variables.
Summary.
Background.
II. LEARNING AND REGULARITY RECOGNITION.
16. Learning by Analyzing Differences.
17. Learning by Explaining Experience.
18. Learning by Correcting Mistakes.
19. Learning by Recording Cases.
20. Learning by Managing Multiple Models.
21. Learning by Building Identification Trees.
22. Learning by Training Neural Nets.
23. Learning by Training Perceptrons.
24. Learning by Training Approximation Nets.
25. Learning by Simulating Evolution.
Induction Heuristics.
Identification.
Summary.
Background.
Identification.
Summary.
Background.
17. Learning by Explaining Experience.
Learning about Why People Act the Way they Do.
Learning about Form and Function.
Matching.
Summary.
Background.
Learning about Form and Function.
Matching.
Summary.
Background.
18. Learning by Correcting Mistakes.
Isolating Suspicious Relations.
Intelligent Knowledge Repair.
Summary.
Background.
Intelligent Knowledge Repair.
Summary.
Background.
19. Learning by Recording Cases.
Recording and Retrieving Raw Experience.
Finding Nearest Neighbors.
A Fast Serial Procedure Finds the Nearest Neighbor in Logarithmic Time.
Parallel Hardware Finds Nearest Neighbors Even Faster.
Summary.
Background.
Finding Nearest Neighbors.
A Fast Serial Procedure Finds the Nearest Neighbor in Logarithmic Time.
Parallel Hardware Finds Nearest Neighbors Even Faster.
Summary.
Background.
20. Learning by Managing Multiple Models.
The Version-Space Method.
Version-Space Characteristics.
Summary.
Background.
Version-Space Characteristics.
Summary.
Background.
21. Learning by Building Identification Trees.
From Data to Identification Trees.
From Trees to Rules.
Summary.
Background.
From Trees to Rules.
Summary.
Background.
22. Learning by Training Neural Nets.
Simulated Neural Nets.
Hill Climbing and Back Propagation.
Back-Propagation Characteristics.
Summary.
Background.
Hill Climbing and Back Propagation.
Back-Propagation Characteristics.
Summary.
Background.
23. Learning by Training Perceptrons.
Perceptrons and Perceptron Learning.
What Perceptrons Can and Cannot Do.
Summary.
Background.
What Perceptrons Can and Cannot Do.
Summary.
Background.
24. Learning by Training Approximation Nets.
Interpolation and Approximation Nets.
Biological Implementation.
Summary.
Background.
Biological Implementation.
Summary.
Background.
25. Learning by Simulating Evolution.
Survival of the Fittest.
Genetic Algorithms.
Survival of the Most Diverse.
Summary.
Background.
Genetic Algorithms.
Survival of the Most Diverse.
Summary.
Background.
III. VISION AND LANGUAGE.
26. Recognizing Objects.
27. Describing Images.
28. Expressing Language Constraints.
29. Responding to Questions and Commands.
Appendix: Relational Databases.
Exercises.
Bibliography.
Index.
Colophon. 0201533774T04062001
Linear Image Combinations.
Establishing Point Correspondence.
Summary.
Background.
Establishing Point Correspondence.
Summary.
Background.
27. Describing Images.
Computing Edge Distance.
Computing Surface Direction.
Summary.
Background.
Computing Surface Direction.
Summary.
Background.
28. Expressing Language Constraints.
The Search for an Economical Theory.
The Search for a Universal Theory.
Competence versus Performance.
Summary.
Background.
The Search for a Universal Theory.
Competence versus Performance.
Summary.
Background.
29. Responding to Questions and Commands.
Syntactic Transition Nets.
Semantic Transition Trees.
Summary.
Background.
Semantic Transition Trees.
Summary.
Background.
Appendix: Relational Databases.
Relational Databases Consist of Tables Containing Records.
Relations Are Easy to Modify.
Records and Fields Are Easy to Extract.
Relations Are Easy to Combine.
Summary.
Relations Are Easy to Modify.
Records and Fields Are Easy to Extract.
Relations Are Easy to Combine.
Summary.
Exercises.
Bibliography.
Index.
Colophon. 0201533774T04062001
