Introduction to Artificial Intelligence

Preface
Developments
1. INTRODUCTION
Introduction
Turing's Test
Natural Intelligence
Evidence from History
Evidence from Introspection
Evidence from the Social Sciences
Evidence from the Biological Sciences
State of Knowledge
The Neuron and the Synapse
Biological Memory
Neural Data Processing
Computers and Simulation
2. "MATHEMATICS, PHENOMENA, MACHINES"
Introduction
On Mathematical Description
The Mathematical Description of Phenomena
Time
Types of Phenomena
Discrete Phenomena
Finite-State Machines
Turing Machines
Simple Turing Machines
Polycephalic Turing Machines
Universal Turing Machines
Limits to Computational Ability
Summary
3. PROBLEM SOLVING
Introduction
Paradigms
General Approaches
Environments
Aptitudes
Evolutionary and Reasoning Programs
"Paradigms for the Concept of "Problem"
Situation-Space
System Inference
"Problem Solvers, Reasoning Programs, and Languages"
General Problem Solver
Reasoning Programs
State-Space (Situation-Space) Problems
Representation
Puzzles
Problem Reduction and Graphs
Summary
Heuristic Search Theory
Need for Search
Search Procedures
Search Trees
"Planning, Reasoning by Analogy, and Learning"
Planning
Reasoning by Analogy
Learning
"Models, Problem Representations, and Levels of Competence"
Models
The Problem of Problem Representation
Levels of Competence
4 GAME PLAYING
Introduction
Games and Their State Spaces
Strategy
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
Checkers
Checker Player
Learning
Learning Situations for Generalization
Book Learning
Results
Chess and GO
Chess
The Game of GO
Poker and Machine Development of Heuristics
Bridge
General Game-Playing Programs
5. PATTERN PERCEPTION
Introduction
Some Basic Definitions and Examples
Eye Systems for Computers
Scene Analysis
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
Introduction
First-Order Predicate Calculus
Theorem-Proving Techniques
Resolution
Groundwork
Clause-Form Equivalents
The Unification Procedure
The Binary Resolution Procedure
Summary
Heuristic Search Strategies
Extensions
Simplification Strategies
Refinement Strategies
Ordering Strategies
Reasoning by Analogy
Solving Problems with Theorem Provers
State-Space
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
Planning
Planner
Automatic Programming
7. SEMANTIC INFORMATION
Introduction
Natural and Artificial Languages
Definitions
Natural Languages
Artificial Languages and Programming Languages
String Languages
"Grammars, Machines, and Extensibility"
"Programs that "Understand" Natural Language"
Five Problems
Syntax
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
Introduction
Motivations
Cellular Automata
Abelian Machine Spaces
Questions of Generality and Equivalence
Self-affecting Systems: Self-reproduction
"Hierarchical, Self-organizing, and Evolutionary Systems"
Conditions
Hierarchical Systems
Self-organizing Systems
Evolutionary Systems
Summary
9. THE HARVEST OF ARTIFICIAL INTELLIGENCE
Introduction
Robots
A Look at Possibilities
Tools and People
Over Mechanization of the World: The Machine as Dictator
The Well-natured Machine
Bibliography
Index