Introduction to Artificial Intelligence: (Dover Books on Mathematics)

Table of Contents:
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