MTU Cork Library Catalogue

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Artificial intelligence / Patrick Henry Winston.

By: Winston, Patrick Henry.
Material type: materialTypeLabelBookSeries: Addison-Wesley series in computer science.Publisher: Reading, Mass. : Addison-Wesley, 1984Edition: 2nd ed.Description: xv, 527 p. : ill. ; 24 cm.ISBN: 0201082594.Subject(s): Artificial intelligenceDDC classification: 006.3
Contents:
The intelligent computer -- Description matching and goal reduction -- Exploiting natural constraints -- Exploring alternatives -- Control metaphors -- Problem-solving paradigms -- Logic and theorem proving -- Representing commonsense knowledge -- Language understanding -- Image understanding -- Learning class descriptions from samples -- Learning rules from experience.
Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
General Lending MTU Bishopstown Library Store Item 006.3 (Browse shelf(Opens below)) 1 Available 00020492
Total holds: 0

Enhanced descriptions from Syndetics:

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.

Bibliography: p. 497-518. - Includes index.

The intelligent computer -- Description matching and goal reduction -- Exploiting natural constraints -- Exploring alternatives -- Control metaphors -- Problem-solving paradigms -- Logic and theorem proving -- Representing commonsense knowledge -- Language understanding -- Image understanding -- Learning class descriptions from samples -- Learning rules from experience.

Table of contents provided by Syndetics

  • 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 eI 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
  • Backg

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