Table of Contents
I. REPRESENTATIONS AND METHODS.
1. The Intelligent Computer.
The Field and the Book. 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.
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.
4. Nets and Basic
Search ¥ Nets and Optimal Search.
Blind Methods. Heuristically Informed Methods. Summary. Background.
5. Nets and Optimal Search.
The Best PathRedundant Paths. Summary. Background.
6. Trees and
Adversarial Search.
Algorithmic Methods. 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.
8. Rules, Substrates, and Cognitive Modeling.
Rule-Based Systems Viewed as Substrate. 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.
10. Frames and
Commonsense.
Thematic-role Frames. 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.
12. Symbolic
Constraints and Propagation.
Propagation of Line Labels through Drawing Junctions. Propagation
of Time-Interval Relations. Five Points of Methodology. Summary.
Background.
13. Logic and Resolution Proof.
Rules of Inference. Resolution Proofs. Summary. Background.
14.
Backtracking and Truth Maintenance.
Chronological and Dependency-Directed Backtracking. Proof by
Constraint Propagation. Summary. Background.
15.
Planning.
Planning Using If-Add-Delete Operators. Planning Using Situation
Variables. Summary. Background.
II. LEARNING AND REGULARITY RECOGNITION.
16. Learning by Analyzing Differences.
Induction Heuristics. 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.
18. Learning by
Correcting Mistakes.
Isolating Suspicious Relations. 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.
20. Learning by Managing Multiple
Models.
The Version-Space Method. Version-Space Characteristics. Summary.
Background.
21. Learning by Building Identification
Trees.
From Data to Identification Trees. 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.
23.
Learning by Training Perceptrons.
Perceptrons and Perceptron Learning. What Perceptrons Can and
Cannot Do. Summary. Background.
24. Learning by Training
Approximation Nets.
Interpolation and Approximation Nets. Biological Implementation.
Summary. Background.
25. Learning by Simulating
Evolution.
Survival of the Fittest. Genetic Algorithms. Survival of the Most
Diverse. Summary. Background.
III. VISION AND LANGUAGE.
26. Recognizing Objects.
Linear Image Combinations. Establishing Point Correspondence.
Summary. Background.
27. Describing Images.
Computing Edge Distance. 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.
29.
Responding to Questions and Commands.
Syntactic Transition Nets. 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.
Exercises.
Bibliography.
Index.
Colophon. 0201533774T04062001About the Author
About Patrick Henry Winston
Well-known author Patrick Henry Winston teaches computer
science and directs the Artificial Intelligence Laboratory at
theMassachusetts Institute of Technology.
0201533774AB04062001
