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ARTIFICIAL INTELLIGENCE - 2nd Edition
by Patrick Henry Wilson
7"x9.5", hard cover, 525 pages, illustrated
Publisher: Addison-Wesley
Weight: 2 lbs.
The first edition of Patrick Henry Winston's Artificial Intelligence is a
classic. Now Professor Winston has completely revised the first edition,
producing this comprehensive new book in which old material has been
purged and new material added to bring things up to date. All surviving
material has been rewritten and rearranged to improve clarity and
coherence.
The book explains the basic theoretical concepts of Artificial
Intelligence in a style that is clear, concise and easy to understand.
In addition, it introduces readers to representative application-oriented
ideas, conveying the excitement surrounding this stimulating and
provocative field.
Special features of this second edition include:
Descriptions of how to do artificial-intelligence work and how to judge
results.
Informally defined procedures that provide precision without presuming
familiarity with a particular programming language.
Explanations of successful applications programs in commercial use.
Chapters on logic and learning.
Many new exercises.
Table of Content:
THE INTELLIGENT COMPUTER
The Field and the Book - What Computers Can Do - Criteria for Success
DESCRIPTION MATCHING AND GOAL REDUCTION
The Key Role of Representation - Analogy Intelligence Tests - Moving
Blocks - Problem Solving and Understanding Knowledge
EXPLOITING NATURAL CONSTRAINTS
Propagating Symbolic Constraints - Propagating Numeric Constraints -
Dependency Directed Backtracking
EXPLORING ALTERNATIVES
Finding Paths - Finding the Best Path - Dealing With Adversaries
CONTROL METAPHORS
Control Choices - Means-Ends Analysis and GPS
PROBLEM-SOLVING PARADIGMS
Generate and Test - Rule-Based Systems for Synthesis - Rule-Bases Systems
for Analysis - Rule-Based Systems for Modeling
Human Thinking
LOGIC AND THEOREM PROVING
Rules of Inference - Resolution Proofs - Planning Operator Sequences -
Proof by Constraint Propagation
REPRESENTING COMMONSENSE KNOWLEDGE
Representation - Inheritance, Demons, Defaults and Perspectives - Frames
and New Stories - Expansion into Primitive Acts -
Abstraction to Summary Units
LANGUAGE UNDERSTANDING
From Sentences to World Models - Parsing Sentences - Forming Thematic-
Role Frames - Interpreting Simple Questions and
Commands - Intellectual Partnerships
IMAGE UNDERSTANDING
From Images to Object Models - Computing Edge Distance - Computing
Surface Direction - Interpreting Simple Binary Images -
Robotics
LEARNING CLASS DESCRIPTIONS FROM SAMPLES
Induction Heuristics - Induction Procedures - Identification
LEARNING RULES FROM EXPERIENCE
Learning Grammar Rules from Sample Sentences - Learning Rulelike
Principles - Matching is Behind Analogy - Learning from
Functional Definitions - Improving Rules Using Learnable Censors
EXERCISES
BIBLIOGRAPHY
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