PART Ⅰ ARTIFICIAL INTELLIGENCE:ITS ROOTS AND SCOPE

1 AI:HISTORY AND APPLICATIONS

1.1 From Eden to ENIAC:Attitudes toward Intelligence,Knowledge,and Human Artifice

1.2 Overview of AI Application Areas

1.3 Artificial Intelligence—A Summary

1.4 Epilogue and References

1.5 Exercises

PART Ⅱ ARTIFICIAL INTELLIGENCE AS REPRESENTATION AND SEARCH

2.1 The Propositional Calculus

2 THE PREDICATE CALCULUS

2.0 Introduction

2.2 The Predicate Calculus

2.3 Using Inference Rules to Produce Predicate Calculus Expressions

2.4 Application:A Logic-Based Financial Advisor

2.5 Epilogue and References

2.6 Exercises

3 STRUCTURES AND STRATEGIES FOR STATE SPACE SEARCH

3.0 Introduction

3.1 Graph Theory

3.2 Strategies for State Space Search

3.3 Using the State Space to Represent Reasoning with the Predicate Calculus

3.4 Epilogue and References

3.5 Exercises

4 HEURISTIC SEARCH

4.0 Introduction

4.1 An Algorithm for Heuristic Search

4.2 Admissibility,Monotonicity,and Informedness

4.3 Using Heuristics in Games

4.4 Complexity Issues

4.5 Epilogue and References

4.6 Exercises

5 CONTROL AND IMPLEMENTATION OF STATE SPACE SEARCH

5.0 Introduction

5.1 Recursion-Based Search

5.2 Pattern-Directed Search

5.3 Production Systems

5.4 The Blackboard Architecture for Problem Solving

5.5 Epilogue and References

5.6 Exercises

PART Ⅲ REPRESENTATION AND INTELLIGENCE:THE AI CHALLENGE

6 KNOWLEDGE REPRESENTATION

6.0 Issues in Knowledge Representation

6.1 A Brief History of AI Representational Systems

6.2 Conceptual Graphs:A Network Language

6.3 Alternatives to Explicit Representation

6.4 Agent Based and Distributed Problem Solving

6.5 Epilogue and References

6.6 Exercises

7 STRONG METHOD PROBLEM SOLVING

7.0 Introduction

7.1 Overview of Expert System Technology

7.2 Rule-Based Expert Systems

7.3 Model-Based,Case Based,and Hybrid Systems

7.4 Planning

7.5 Epilogue and References

7.6 Exercises

8.0 Introduction

8 REASONING IN UNCERTAIN SITUATIONS

8.1 Logic-Based Abductive Inference

8.2 Abduction:Alternatives to Logic

8.3 The Stochastic Approach to Uncertainty

8.4 Epilogue and References

8.5 Exercises

PART Ⅳ MACHINE LEARNING

9 MACHINE LEARNING:SYMBOL-BASED

9.1 A Framework for Symbol-based Learning

9.2 Version Space Search

9.3 The ID3 Decision Tree Induction Algorithm

9.4 Inductive Bias and Learnability

9.5 Knowledge and Learning

9.6 Unsupervised Learning

9.7 Reinforcement Learning

9.8 Epilogue and References

9.9 Exercises

10.0 Introduction

10 MACHINE LEARNING:CONNECTIONIST

10.1 Foundations for Connectionist Networks

10.2 Perceptron Learning

10.3 Backpropagation Learning

10.4 Competitive Learning

10.5 Hebbian Coincidence Learning

10.6 Attractor Networks or

10.7 Epilogue and References

10.8 Exercises

11 MACHINE LEARNING:SOCIAL AND EMERGENT

11.0 Social and Emergent Models of Learning

11.1 The Genetic Algorithm

11.2 Classifier Systems and Genetic Programming

11.3 Artificial Life and Society-Based Learning

11.4 Epilogue and References

11.5 Exercises

PART Ⅴ ADVANCED TOPICS FOR AI PROBLEM SOLVING

12 AUTOMATED REASONING

12.0 Introduction to Weak Methods in Theorem Proving

12.1 The General Problem Solver and Difference Tables

12.2 Resolution Theorem Proving

12.3 PROLOG and Automated Reasoning

12.4 Further Issues in Automated Reasoning

12.5 Epilogue and References

12.6 Exercises

13 UNDERSTANDING NATURAL LANGUAGE

13.0 Role of Knowledge in Language Understanding

13.1 Deconstructing Language:A Symbolic Analysis

13.7 Exercises

13.2 Syntax

13.3 Syntax and Knowledge with ATN Parsers

13.4 Stochastic Tools for Language Analysis

13.5 Natural Language Applications

13.6 Epilogue and References

PART Ⅵ LANGUAGES AND PROGRAMMING TECHNIQUES FOR ARTIFICIAL INTELLIGENCE

14.0 Introduction

9.0 Introduction

14 AN INTRODUCTION TO PROLOG

14.1 Syntax for Predicate Calculus Programming

14.2 Abstract Data Types(ADTs)in PROLOG

14.3 A Production System Example in PROLOG

14.4 Designing Alternative Search Strategies

14.5 A PROLOG Planner

14.6 PROLOG:Meta-Predicates,Types,and Unification

14.7 Meta-Interpreters in PROLOG

14.8 Learning Algorithms in PROLOG

14.9 Natural Language Processing in PROLOG

14.10 Epilogue and References

14.11 Exercises

15 AN INTRODUCTION TO LISP

15.0 Introduction

15.1 LISP:A Brief Overview

15.2 Search in LISP:A Functional Approach to the Farmer,Wolf,Goat,and Cabbage Problem

15.3 Higher-Order Functions and Procedural Abstraction

15.4 Search Strategies in LISP

15.5 Pattern Matching in LISP

15.6 A Recursive Unification Function

15.7 Interpreters and Embedded Languages

15.8 Logic Programming in LISP

15.9 Streams and Delayed Evaluation

15.15 An Expert System Shell in LISP

15.11 Semantic Networks and Inheritance in LISP

15.12 Object-Oriented Programming Using CLOS

15.13 Learning in LISP:The ID3 Algorithm

15.14 Epilogue and References

15.15 Exercises

PART Ⅶ EPILOGUE

16 ARTIFICIAL INTELLIGENCE AS EMPIRICAL ENQUIRY

16.0 Introduction

16.1 Artificial Intelligence:A Revised Definition

16.2 The Science of Intelligent Systems

16.3 AI:Current Issues and Future Directions

16.4 Epilogue and References

Bibliography

Author Index

Subject Index