Artificial Intelligence

Artificial Intelligence

1st Edition - January 28, 1975
  • Author: Earl B. Hunt
  • eBook ISBN: 9781483263175

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Description

Artificial Intelligence provides information pertinent to the fundamental aspects of artificial intelligence. This book presents the basic mathematical and computational approaches to problems in the artificial intelligence field. Organized into four parts encompassing 16 chapters, this book begins with an overview of the various fields of artificial intelligence. This text then attempts to connect artificial intelligence problems to some of the notions of computability and abstract computing devices. Other chapters consider the general notion of computability, with focus on the interaction between computability theory and artificial intelligence. This book discusses as well the concepts of pattern recognition, problem solving, and machine comprehension. The final chapter deals with the study of machine comprehension and reviews the fundamental mathematical and computing techniques underlying artificial intelligence research. This book is a valuable resource for seniors and graduate students in any of the computer-related sciences, or in experimental psychology. Psychologists, general systems theorists, and scientists will also find this book useful.

Table of Contents


  • Preface

    Acknowledgments

    I Introduction

    Chapter I The Scope of Artificial Intelligence

    1.0 Is There Such a Thing?

    1.1 Problem Solving

    1.2 Pattern Recognition

    1.3 Game Playing and Decision Making

    1.4 Natural Language and Machine Comprehension

    1.5 Self-Organizing Systems

    1.6 Robotology

    Chapter II Programming, Program Structure, and Computability

    2.0 The Relevance of Computability

    2.1 Computations on Strings

    2.2 Formal Grammars

    2.3 Turing Machines

    2.4 Linear Bounded Automata and Type 1 Languages

    2.5 Pushdown Automata and Type 2 Languages

    2.6 Finite Automata and Regular (Type 3) Languages

    2.7 Summary and Comments on Practicality

    II Pattern Recognition

    Chapter III General Considerations in Pattern Recognition

    3.0 Classification

    3.1 Categorizing Pattern-Recognition Problems

    3.2 Historical Perspective and Current Issues

    Chapter IV Pattern Classification and Recognition Methods Based on Euclidean Description Spaces

    4.0 General

    4.1 Bayesian Procedures in Pattern Recognition

    4.2 Classic Statistical Approach to Pattern Recognition and Classification

    4.3 Classification Based on Proximity of Descriptions

    4.4 Learning Algorithms

    4.5 Clustering

    Chapter V Non-Euclidean Parallel Procedures: The Perceptron

    5.0 Introduction and Historical Comment

    5.1 Terminology

    5.2 Basic Theorems for Order-Limited Perceptrons

    5.3 Substantive Theorems for Order-Limited Perceptrons

    5.4 Capabilities of Diameter-Limited Perceptrons

    5.5 The Importance of Perceptron Analysis

    Chapter VI Sequential Pattern Recognition

    6.0 Sequential Classification

    6.1 Definitions and Notation

    6.2 Bayesian Decision Procedures

    6.3 Bayesian Optimal Classification Procedures Based on Dynamic Programming

    6.4 Approximations Based on Limited Look Ahead Algorithms

    6.5 Convergence in Sequential Pattern Recognition

    Chapter VII Grammatical Pattern Classification

    7.0 The Linguistic Approach to Pattern Analysis

    7.1 The Grammatical Inference Problem

    7.2 Grammatical Analysis Applied to Two-Dimensional Images

    Chapter VIII Feature Extraction

    8.0 General

    8.1 Formalization of the Factor-Analytic Approach

    8.2 Formalization of the Binary Measurement Case

    8.3 Constructive Heuristics for Feature Detection

    8.4 An Experimental Study of Feature Generation in Pattern Recognition

    8.5 On Being Clever

    III Theorem Proving and Problem Solving

    Chapter IX Computer Manipulable Representations in Problem Solving

    9.0 The Use of Representations

    9.1 A Typology of Representations

    9.2 Combining Representations

    Chapter X Graphic Representations in Problem Solving

    10.0 Basic Concepts and Definitions

    10.1 Algorithms for Finding a Minimal Path to a Single Goal Node

    10.2 An "Optimal" Ordered Search Algorithm

    10.3 Tree Graphs and Their Use

    Chapter XI Heuristic Problem-Solving Programs

    11.0 General Comments

    11.1 Terminology

    11.2 The General Problem Solver (GPS)

    11.3 The Fortran Deductive System-Automatic Generation of Operator-Difference Tables

    11.4 Planning

    Chapter XII Theorem Proving

    12.0 Theorem Proving Based on Herbrand Proof Procedures

    12.1 The Resolution Principle

    12.2 Simple Refinement Strategies

    12.3 Ancestory Strategies

    12.4 Syntactic Strategies

    12.5 Semantic Strategies

    12.6 Heuristics

    12.7 Quantification

    12.9 Problems and Future Development

    IV Comprehension

    Chapter XIII Computer Perception

    13.0 The Problem of Perception

    13.1 Vision

    13.2 Perception of Speech by Computer

    Chapter XIV Question Answering

    14.0 The Problem

    14.1 Data Structures

    14.2 Deductive Inference in Information Retrieval

    14.3 Comprehension without logic

    Chapter XV Comprehension of Natural Language

    15.0 The Problem

    15.1 Natural Language: The Mathematical Model

    15.2 The Psychological Model

    Chapter XVI Review and Prospectus

    16.0 Things Done and Undone

    16.1 Some Problems of Philosophy

    16.2 A General Theory of Thought

    References

    Author Index

    Subject Index

Product details

  • No. of pages: 480
  • Language: English
  • Copyright: © Academic Press 1975
  • Published: January 28, 1975
  • Imprint: Academic Press
  • eBook ISBN: 9781483263175

About the Author

Earl B. Hunt

About the Editors

Edward C. Carterette

Morton P. Friedman