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Introduction To Artificial Intelligence 2ND Editionby Philip C Jackson
Synopses & Reviews
Can computers think? Can they use reason to develop their own concepts, solve complex problems, play games, understand our languages? This comprehensive survey of artificial intelligence ― the study of how computers can be made to act intelligently ― explores these and other fascinating questions. Introduction to Artificial Intelligence presents an introduction to the science of reasoning processes in computers, and the research approaches and results of the past two decades. You'll find lucid, easy-to-read coverage of problem-solving methods, representation and models, game playing, automated understanding of natural languages, heuristic search theory, robot systems, heuristic scene analysis and specific artificial-intelligence accomplishments. Related subjects are also included: predicate-calculus theorem proving, machine architecture, psychological simulation, automatic programming, novel software techniques, industrial automation and much more.
A supplementary section updates the original book with major research from the decade 1974-1984. Abundant illustrations, diagrams and photographs enhance the text, and challenging practice exercises at the end of each chapter test the student's grasp of each subject.The combination of introductory and advanced material makes Introduction to Artificial Intelligence ideal for both the layman and the student of mathematics and computer science. For anyone interested in the nature of thought, it will inspire visions of what computer technology might produce tomorrow.
Comprehensive survey of artificial intelligence — the study of how computers can be made to act intelligently. Includes introductory and advanced material. Extensive notes updating the main text. 132 illustrations.
This comprehensive, easy-to-read survey of how machines (computers) can be made to act intelligently explores problem-solving methods, representation and models, game playing, automated understanding of natural languages, heuristic scene analysis, specific artificial intelligence accomplishments and other related topics. With 132 illustrations.
Table of Contents
Evidence from History
Evidence from Introspection
Evidence from the Social Sciences
Evidence from the Biological Sciences
State of Knowledge
The Neuron and the Synapse
Neural Data Processing
Computers and Simulation
2. "MATHEMATICS, PHENOMENA, MACHINES"
On Mathematical Description
The Mathematical Description of Phenomena
Types of Phenomena
Simple Turing Machines
Polycephalic Turing Machines
Universal Turing Machines
Limits to Computational Ability
3. PROBLEM SOLVING
Evolutionary and Reasoning Programs
"Paradigms for the Concept of "Problem"
"Problem Solvers, Reasoning Programs, and Languages"
General Problem Solver
State-Space (Situation-Space) Problems
Problem Reduction and Graphs
Heuristic Search Theory
Need for Search
"Planning, Reasoning by Analogy, and Learning"
Reasoning by Analogy
"Models, Problem Representations, and Levels of Competence"
The Problem of Problem Representation
Levels of Competence
4 GAME PLAYING
Games and Their State Spaces
Game Trees and Heuristic Search
Game Trees and Minimax Analysis
Static Evaluations and Backed-up Evaluations
The Alpha-Beta Technique
Generating (Searching) Game Trees
Learning Situations for Generalization
Chess and GO
The Game of GO
Poker and Machine Development of Heuristics
General Game-Playing Programs
5. PATTERN PERCEPTION
Some Basic Definitions and Examples
Eye Systems for Computers
Picture Enhancement and Line Detection
Perception of Regions
Perception of Objects
Learning to Recognize Structures of Simple Objects
Some Problems for Pattern Perception Systems
6. THEOREM PROVING
First-Order Predicate Calculus
The Unification Procedure
The Binary Resolution Procedure
Heuristic Search Strategies
Reasoning by Analogy
Solving Problems with Theorem Provers
Predicate-Calculus Descriptions of State-Space Problems
"Path Finding, Example Generation, Constructive Proofs, Answer Extraction"
Applications to Real-World Problems
Theorem Proving in Planning and Automatic Programming
7. SEMANTIC INFORMATION
Natural and Artificial Languages
Artificial Languages and Programming Languages
"Grammars, Machines, and Extensibility"
"Programs that "Understand" Natural Language"
Recursive Approaches to Syntax
Semantics and Inference
Generation and Integration
Some Conversations with Computers
Language and Perception
Networks of Question-Answering Programs
Pattern Recognition and Grammatical Inference
"Communications, Teaching, and Learning"
8. PARALLEL PROCESSING AND EVOLUTIONARY SYSTEMS
Abelian Machine Spaces
Questions of Generality and Equivalence
Self-affecting Systems: Self-reproduction
"Hierarchical, Self-organizing, and Evolutionary Systems"
9. THE HARVEST OF ARTIFICIAL INTELLIGENCE
A Look at Possibilities
Tools and People
Over Mechanization of the World: The Machine as Dictator
The Well-natured Machine
What Our Readers Are Saying
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