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Artificial intelligence

Author: Patrick Henry Winston
Publisher: Reading, Mass. : Addison-Wesley Pub. Co., [1993]
Edition/Format:   Print book : English : 3rd ed., repr. with corrections 1993View all editions and formats
Database:WorldCat
Summary:

Explains how it is possible for computers to reason and perceive, thus introducing the field called Artificial Intelligence. This book helps in learning why the field is important, both as a branch  Read more...

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Genre/Form: manuel
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Patrick Henry Winston
ISBN: 0201533774 9780201533774 0201600862 9780201600865
OCLC Number: 37936478
Description: xxv, 737 p. : ill. ; 25 cm
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 [yen] 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. 0201533774T04062001
Responsibility: Patrick Henry Winston.
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