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Artificial Intelligence

ISBN-10: 0201533774

ISBN-13: 9780201533774

Edition: 3rd 1992

Authors: Patrick H. Winston

List price: $164.40
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Description:

This is an eagerly awaited revision of the single bestselling introduction to Artificial Intelligence ever published. It retains the best features of the earlier works including superior readability, currency, and excellence in the selection of the examples.
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Book details

List price: $164.40
Edition: 3rd
Copyright year: 1992
Publisher: Addison Wesley
Publication date: 4/30/1992
Binding: Paperback
Pages: 764
Size: 7.75" wide x 9.50" long x 1.50" tall
Weight: 3.168
Language: English

Representations and Methods
The Intelligent Computer
The Field and the Book
This Book Has Three Parts
What Artificial Intelligence Can Do
Criteria for Success
Summary Background
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
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
Nets and Basic Search eI Nets and Optimal Search
Blind Methods
Heuristically Informed Methods
Summary
Background
Nets and Optimal Search
The Best PathRedundant Paths
Summary
Background
Trees and Adversarial Search
Algorithmic Methods
Heuristic Methods
Summary
Background
Rules and Rule Chaining
Rule-Based Deduction Systems
Rule-Based Reaction Systems
Procedures for Forward and Backward Chaining
Summary
Background
Rules, Substrates, and Cognitive Modeling
Rule-Based Systems Viewed as Substrate
Rule-Based Systems Viewed as Models for Human Problem Solving
Summary
Background
Frames and Inheritance
Frames, Individuals, and Inheritance
Demon ProceduresFrames, Events, and Inheritance
Summary
Background
Frames and Commonsense
Thematic-role Frames
Examples Using Take Illustrate How Constraints Interact
Expansion into Primitive Actions
Summary
Background
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
Symbolic Constraints and Propagation
Propagation of Line Labels through Drawing Junctions
Propagation of Time-Interval Relations
Five Points of Methodology
Summary
Background
Logic and Resolution Proof
Rules of Inference
Resolution Proofs
Summary
Background
Backtracking and Truth Maintenance
Chronological and Dependency-Directed Backtracking
Proof by Constraint Propagation
Summary
Background
Planning
Planning Using If-Add-Delete Operators
Planning Using Situation Variables
Summary
Background
Learning and Regularity Recognition
Learning by Analyzing Differences
Induction Heuristics
Identification
Summary
Background
Learning by Explaining Experience
Learning about Why People Act the Way they Do
Learning about Form and function
Matching
Summary
Background
Learning by Correcting Mistakes
Isolating Suspicious Relations
Intelligent Knowledge Repair
Summary
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