Computational Intelligence A Logical Approach

This introduction to artificial intelligence (AI) weaves a unifying themeamongst the core issues that underlie the discipline of AI and places them intoa coherent, cohesive, and intellectually defensible framework. This unifyingtheme features an intelligent agent acting in its environment. The book providesa solid foundation upon which readers can build an understanding of theprogression of development in AI, covering fundamental concepts in depth. Itsapproach clarifies and integrates representation and reasoning fundamentals,leading readers from simple to complex ideas with clear motivation. The authorsdevelop AI representation schemes and describe their use for interesting andpopular applications, including natural language vision, expert systems, gameplaying, neural networks, robotics, and more.

Preface

xv

1 Computational Intelligence and Knowledge

1

(22)

1.1 What Is Computational Intelligence?

1

(6)

1.2 Agents in the World

7

(2)

1.3 Representation and Reasoning

9

(2)

1.4 Applications

11

(8)

1.5 Overview

19

(1)

1.6 References and Further Reading

20

(1)

1.7 Exercises

21

(2)

2 A Representation and Reasoning System

23

(46)

2.1 Introduction

23

(1)

2.2 Representation and Reasoning Systems

23

(4)

2.3 Simplifying Assumptions of the Initial RRS

27

(2)

2.4 Datalog

29

(2)

2.5 Semantics

31

(9)

2.6 Questions and Answers

40

(6)

2.7 Proofs

46

(12)

2.8 Extending the Language with Function Symbols

58

(5)

2.9 References and Further Reading

63

(1)

2.10 Exercises

63

(6)

3 Using Definite Knowledge

69

(44)

3.1 Introduction

69

(1)

3.2 Case Study: House Wiring

70

(5)

3.3 Databases and Recursion

75

(4)

3.4 Verification and Limitations

79

(2)

3.5 Case Study: Representing Abstract Concepts

81

(5)

3.6 Case Study: Representing Regulatory Knowledge

86

(5)

3.7 Applications in Natural Language Processing

91

(13)

3.8 References and Further Reading

104

(1)

3.9 Exercises

104

(9)

4 Searching

113

(56)

4.1 Why Search?

113

(1)

4.2 Graph Searching

114

(5)

4.3 A Generic Searching Algorithm

119

(6)

4.4 Blind Search Strategies

125

(7)

4.5 Heuristic Search

132

(6)

4.6 Refinements to Search Strategies

138

(9)

4.7 Constraint Satisfaction Problems

147

(16)

4.8 References and Further Reading

163

(1)

4.9 Exercises

163

(6)

5 Representing Knowledge

169

(30)

5.1 Introduction

169

(1)

5.2 Defining a Solution

170

(4)

5.3 Choosing a Representation Language

174

(6)

5.4 Mapping from Problem to Representation

180

(12)

5.5 Choosing an Inference Procedure

192

(3)

5.6 References and Further Reading

195

(1)

5.7 Exercises

196

(3)

6 Knowledge Engineering

199

(36)

6.1 Introduction

199

(1)

6.2 Knowledge-Based System Architecture

200

(2)

6.3 Meta-Interpreters

202

(10)

6.4 Querying the User

212

(5)

6.5 Explanation

217

(4)

6.6 Debugging Knowledge Bases

221

(5)

6.7 A Meta-Interpreter with Search

226

(4)

6.8 Unification

230

(3)

6.9 References and Further Reading

233

(1)

6.10 Exercises

233

(2)

7 Beyond Definite Knowledge

235

(46)

7.1 Introduction

235

(1)

7.2 Equality

235

(6)

7.3 Integrity Constraints

241

(7)

7.4 Complete Knowledge Assumption

248

(8)

7.5 Disjunctive Knowledge

256

(12)

7.6 Explicit Quantification

268

(2)

7.7 First-Order Predicate Calculus

270

(5)

7.8 Modal Logic

275

(2)

7.9 References and Further Reading

277

(1)

7.10 Exercises

278

(3)

8 Actions and Planning

281

(38)

8.1 Introduction

281

(6)

8.2 Representations of Actions and Change

287

(11)

8.3 Reasoning with World Representations

298

(17)

8.4 References and Further Reading

315

(1)

8.5 Exercises

316

(3)

9 Assumption-Based Reasoning

319

(26)

9.1 Introduction

319

(2)

9.2 An Assumption-Based Reasoning Framework

321

(2)

9.3 Default Reasoning

323

(9)

9.4 Abduction

332

(3)

9.5 Evidential and Causal Reasoning

335

(4)

9.6 Algorithms for Assumption-Based Reasoning

339

(3)

9.7 References and Further Reading

342

(1)

9.8 Exercises

343

(2)

10 Using Uncertain Knowledge

345

(52)

10.1 Introduction

345

(1)

10.2 Probability

346

(15)

10.3 Independence Assumptions

361

(20)

10.4 Making Decisions Under Uncertainty

381

(13)

10.5 References and Further Reading

394

(1)

10.6 Exercises

395

(2)

11 Learning

397

(46)

11.1 Introduction

397

(6)

11.2 Learning as Choosing the Best Representation

403

(11)

11.3 Case-Based Reasoning

414

(2)

11.4 Learning as Refining the Hypothesis Space

416

(8)

11.5 Learning Under Uncertainty

424

(9)

11.6 Explanation-Based Learning

433

(4)

11.7 References and Further Reading

437

(1)

11.8 Exercises

438

(5)

12 Building Situated Robots

443

(18)

12.1 Introduction

443

(1)

12.2 Robotic Systems

444

(2)

12.3 The Agent Function

446

(1)

12.4 Designing Robots

447

(2)

12.5 Uses of Agent Models

449

(1)

12.6 Robot Architectures

450

(1)

12.7 Implementing a Controller

451

(6)

12.8 Robots Modeling the World

457

(1)

12.9 Reasoning in Situated Robots

458

(1)

12.10 References and Further Reading

459

(1)

12.11 Exercises

460

(1)

Appendix A Glossary

461

(16)

Appendix B The Prolog Programming Language

477

(14)

B.1 Introduction

477

(1)

B.2 Interacting with Prolog

478

(1)

B.3 Syntax

479

(2)

B.4 Arithmetic

481

(2)

B.5 Database Relations

483

(2)

B.6 Returning All Answers

485

(2)

B.7 Input and Output

487

(1)

B.8 Controlling Search

488

(3)

Appendix C Some More Implemented Systems

491

(42)

C.1 Bottom-Up Interpreters

491

(7)

C.2 Top-Down Interpreters

498

(9)

C.3 Constraint Satisfaction Problem Solver

507

(4)

C.4 Neural Network Learner

511

(4)

C.5 Partial-Order Planner

515

(6)

C.6 Implementing Belief Networks

521

(8)

C.7 Robot Controller

529

(4)

Bibliography

533

(16)

Index

549

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Computational Intelligence A Logical Approach

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Table of Contents

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