Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain, 1st Edition (Hardback) book cover

Artificial Intelligence and Soft Computing

Behavioral and Cognitive Modeling of the Human Brain, 1st Edition

By Amit Konar

CRC Press

816 pages

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pub: 1999-12-08
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Description

With all the material available in the field of artificial intelligence (AI) and soft computing-texts, monographs, and journal articles-there remains a serious gap in the literature. Until now, there has been no comprehensive resource accessible to a broad audience yet containing a depth and breadth of information that enables the reader to fully understand and readily apply AI and soft computing concepts.

Artificial Intelligence and Soft Computing fills this gap. It presents both the traditional and the modern aspects of AI and soft computing in a clear, insightful, and highly comprehensive style. It provides an in-depth analysis of mathematical models and algorithms and demonstrates their applications in real world problems.

Beginning with the behavioral perspective of "human cognition," the text covers the tools and techniques required for its intelligent realization on machines. The author addresses the classical aspects-search, symbolic logic, planning, and machine learning-in detail and includes the latest research in these areas. He introduces the modern aspects of soft computing from first principles and discusses them in a manner that enables a beginner to grasp the subject. He also covers a number of other leading aspects of AI research, including nonmonotonic and spatio-temporal reasoning, knowledge acquisition, and much more.

Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain is unique for its diverse content, clear presentation, and overall completeness. It provides a practical, detailed introduction that will prove valuable to computer science practitioners and students as well as to researchers migrating to the subject from other disciplines.

Table of Contents

INTRODUCTION TO AI AND SOFT COMPUTING

Evolution of Computing

Defining AI

General Problem Solving Approaches in AI

The Disciplines of AI

A Brief History of AI

Characteristic Requirement for the Realization of Intelligent Systems

Programming Languages for AI

Architecture for AI Machines

Objective and Scope of the Book

Summary

THE PSYCHOLOGICAL PERSPECTIVE OF COGNITION

Introduction

The Cognitive Perspective of Pattern Recognition

Cognitive Models of Memory

Mental Imagery

Understanding a Problem

A Cybernetic View to Cognition

Scope of Realization of Cognition in AI

Summary

PRODUCTION SYSTEMS

Introduction

Production Rules

The Working Memory

The Control Unit / Interpreter

Conflict Resolution Strategies

An Alternative Approach for Conflict Resolution

An Illustrative Production System

The RETE Match Algorithm

Types of Production Systems

Forward versus Backward Production Systems

General Merits of a Production System

Knowledge Base Optimization in a Production System

Conclusions

PROBLEM SOLVING BY INTELLIGENT SEARCH

Introduction

General Problem Solving Approaches

Heuristic Search

Adversary Search

Conclusions

THE LOGIC OF PROPOSITIONS AND PREDICATES

Introduction

Formal Definitions

Tautologies in Propositional Logic

Theorem Proving by Propositional Logic

Resolution in Propositional Logic

Soundness and Completeness of Propositional Logic

Predicate Logic

Writing a Sentence into Clause Forms

Unification of Predicates

Robinson's Inference Rule

Different Types of Resolution

Semi-Decidability

Soundness and Completeness of Predicate Logic

Conclusions

PRINCIPLES OF LOGIC PROGRAMMING

Introduction to PROLOG Programming

Logic Programs - A Formal Definition

A Scene Interpretation Program

Illustrating Backtracking by flow of Satisfaction Diagrams

The SLD Resolution

Controlling Backtracking by CUT

The NOT Predicate

Negation as a Failure in Extended Logic Programs

Fixed Points in Non-Horn Clause Based Programs

Constraint Logic Programming

Conclusions

DEFAULT AND NON-MONOTONIC REASONING

Introduction

Monotonic versus Non-Monotonic Logic

Non-Monotonic Resoning Using NML-I

Fixed Points in Non-Monotonic Reasoning

Non-Monotonic Resoning Using NML-II

Truth Maintenance System

Default Reasoning

The Closed World Assumption

Circumscription

Auto-Epistemic Logic

Conclusions

STRUCTURED APPROACH TO KNOWLEDGE REPRESENTATION

Introduction

Semantic Nets

Inheritance in Semantic Nets

Manipulating Monotonic and Default Inheritance in Semantic Nets

Defeasible Reasoning in Semantic Nets

Frames

Inheritance in Tangled Frames

Petri nets

Conceptual Dependency

Scripts

Conclusions

DEALING WITH IMPRECISION AND UNCERTAINTY

Introduction

Probabilistic Reasoning

Certainty Factor Based Reasoning

Fuzzy Reasoning

Comparison of the Proposed Models

STRUCTURED APPROACH TO FUZZY REASONING

Introduction

Structural Model of Fuzzy FPN and Reachability Analysis

Behavioral Model of FPN and Stability Analysis

Forward Reasoning in FPN

Backward Reasoning in FPN

Bi-directional IFF Type Reasoning and Reciprocity

Fuzzy Modus Tollens and Duality

Non-Monotonic Reasoning in an FPN

Conclusions

REASONING WITH SPACE AND TIME

Introduction

Spatial Reasoning

Spatial Relationships among Components of an Object

Fuzzy Spatial Relationships among Objects

Temporal Reasoning by Situation Calculus

Propositional Temporal Logic

Interval Temporal Logic

Reasoning with Both Space and Time

Conclusions

INTELLIGENT PLANNING

Introduction

Planning with If-Add-Delete Operators

Least Commitment Planning

Hierarchical Task Network Planning

Multi-agent Planning

The Flowshop Scheduling Problem

Summary

MACHINE LEARNING TECHNIQUES

Introduction

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Learning by Inductive Logic Programming

Computational Learning Theory

Summary

MACHINE LEARNING USING NEURAL NETS

Biological Neural Nets

Artificial Neural Nets

Topology of Artificial Neural Nets

Learning Using Neural Nets

The Back-Propagation Training Algorithm

Widrow-Hoff's Multi-Layers ADALINE Models

Hopfield Neural Net

Associative Memory

Fuzzy Neural Nets

Self-Organizing Neural Net

Adaptive Resonance Theory (ART)

Applications of Artificial Neural Nets

GENETIC ALGORITHMS

Introduction

Deterministic Explanation of Holland's Observation

Stochastic Explanation of GA

The Markov Model for Convergence Analysis

Application of GA in Optimization Problems

Application of GA in Machine Learning

Applications of GA in Intelligent Search

Genetic Programming

Conclusions

REALIZING COGNITION USING FUZZY NEURAL NETS

Cognitive Maps

Learning by a Cognitive Map

The Recall in a Cognitive Map

Stability Analysis

Cognitive Learning with FPN

Applications in Autopilots

Generation of Control Commands by a Cognitive Map

Task Planning and Coordination

Putting it all Together

Conclusions and Future Directions

VISUAL PERCEPTION

Introduction

Low level Vision

Medium Level Vision

High Level Vision

Conclusions

LINGUISTIC PERCEPTION

Introduction

Syntactic Analysis

Augmented Transition Network Parsers

Semantic Interpretation by Case Grammar and Type Hierarchy

Discourse and Pragmatic Analysis

Applications of Natural Language Understanding

PROBLEM SOLVING BY CONSTRAINT SATISFACTION

Introduction

Formal Definitions

Constraint Propagation in Networks

Determining Satisfiability of CSP

Constraint Logic Programming

Geometric Constraint Satisfaction

Conclusions

ACQUISITION OF KNOWLEDGE

Introduction

Manual Approach for Knowledge Acquisition

Knowledge Fusion from Multiple Experts

Machine Learning Approach for Knowledge Acquisition

Knowledge Refinement by Hebbian Learning

Conclusions

VALIDATION, VERIFICATION AND MAINTENANCE ISSUES

Introduction

Valildation of Expert Systems

Verification of Knowledge Based System

Maintenance of Knowledge Based Systems

Conclusions

PARALLEL AND DISTRIBUTED ARCHITECTURE FOR INTELLIGENT SYSTEMS

Introduction

Salient Features of AI Machines

Parallelism in Heuristic Search

Parallelism at Knowledge Representational Level

Parallel Architecture for Logic Programming

Conclusions

CASE STUDY I: BUILDING A SYSTEM FOR CRIMINAL INVESTIGATION

An Overview of the Proposed Scheme

Introduction to Image Matching

Fingerprint Classification and Matching

Identification of the Suspects from Voice

Identification of the Suspects from Incidental Descriptions

Conclusions

CASE STUDY II: REALIZATION OF COGNITION FOR MOBILE ROBOTS

Mobile Robots

Scope of Realization of Cognition on Mobile Robots

Knowing the Robot's World

Types of Navigational Planning Problems

Offline Planning by Generalized Voronoi Diagram (GVD)

Path Traversal Optimization Problem

Self-Orgainizing Map (SOM)

Online Navigation by Modular Back-Propagation Neural Nets

Coordination among Sub-Modules in a Mobile Robot

An Application in a Soccer Playing Robot

The Expectations from the Readers

APPENDIX A: How to Run the Sample Programs?

APPENDIX B: Derivation of the Back-propagation Algorithm

APPENDIX C: Proof of the Theorems of Chapter 10

INDEX

Subject Categories

BISAC Subject Codes/Headings:
COM051240
COMPUTERS / Software Development & Engineering / Systems Analysis & Design
TEC007000
TECHNOLOGY & ENGINEERING / Electrical