Computational Intelligence A Logical Approach

by Poole, David; Mackworth, Alan; Goebel, Randy

• ISBN13: 9780195102703
• ISBN10: 0195102703
• Format: Hardcover
• Copyright: 1998-01-08
• Publisher: Oxford University Press

Summary

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.

Table of Contents

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