Computational Intelligence: A Logical Approach / Edition 1

by David Poole, Alan Mackworth, Randy Goebel

Overview

Computational Intelligence: A Logical Approach provides a unique and integrated introduction to artificial intelligence. It weaves a unifying theme—an intelligent agent acting in its environment— through the core issues of AI, placing them into a coherent framework. Rather than giving a surface treatment of an overwhelming number of topics, it covers fundamental concepts in depth, providing a foundation on which students can build an understanding of modern AI. This logical approach clarifies and integrates representation and reasoning fundamentals, leading students from simple to complex ideas with clear motivation. The authors develop AI representation schemes and describe their uses for diverse applications, from autonomous robots to diagnostic assistants to infobots that find information in rich information sources. The authors' website (http://www.cs.ubc.ca/spider/poole/ci.html) offers extensive support for the text, including source code, interactive Java scripts, various pedagogical aids, and an interactive environment for developing and debugging knowledge bases.
Ideal for upper-level undergraduate and introductory graduate courses in artificial intelligence, Computational Intelligence encourages students to explore, implement, and experiment with a series of progressively richer representations that capture the essential features of more and more demanding tasks and environments.

Product Details

ISBN-13:	9780195102703
Publisher:	Oxford University Press
Publication date:	01/08/1998
Edition description:	New Edition
Pages:	576
Product dimensions:	10.29(w) x 7.25(h) x 1.49(d)

About the Author

both at University of British Columbia

University of Alberta

Table of Contents

Preface1. Computational Intelligence and Knowledge1.1. What is Computational Intelligence? 1.2. Agents in the World1.3. Representation and Reasoning1.4. Applications1.5. Overview1.6. References and Further Reading1.7. Exercises2. A Representation and Reasoning System2.1. Introduction2.2. Representation and Reasoning Systems2.3. Simplifying Assumptions of the Initial RRS2.4. Datalog2.5. Semantics2.6. Questions and Answers2.7. Proofs2.8. Extending the Language with Function Symbols2.9. References and Further Reading2.10. Exercises3. Using Definite Knowledge3.1. Introduction3.2. Case Study: House Wiring3.3. Databases and Recursion3.4. Verification and Limitations3.5. Case Study: Representing Abstract Concepts3.6. Case Study: Representing Regulatory Knowledge3.7. Applications in Natural Language Processing3.8. References and Further Reading3.9. Exercises4. Searching4.1. Why Search? 4.2. Graph Searching4.3. A Generic Searching Algorithm4.4. Blind Search Strategies4.5. Heuristic Search4.6. Refinements to Search Strategies4.7. Constraint Satisfaction Problems4.8. References and Further Reading4.9. Exercises5. Representing Knowledge5.1. Introduction5.2. Defining a solution5.3. Choosing a Representation Language5.4. Mapping from Problem to Representation5.5. Choosing an Inference Procedure5.6. References and Further Reading5.7. Exercises6. Knowledge Engineering6.1. Introduction6.2. Knowledge-Based System Architecture6.3. Meta-interpreters6.4. Querying the User6.5. Explanation6.6. Debugging Knowledge Bases6.7. A Meta-interpreter with Search6.8. Unification6.9. References and Further Reading6.10. Exercises7. Beyond Definite Knowledge7.1. Introduction7.2. Equality7.3. Integrity Constraints7.4. Complete Knowledge Assumption7.5. Disjunctive Knowledge7.6. Explicit Quantification7.7. First-Order Predicate Calculus7.8. Modal Logic7.9. References and Further Reading7.10. Exercises8. Actions and Planning8.1. Introduction8.2. Representations of Actions and Change8.3. Reasoning with World Representations8.4. References and Further Reading8.5. Exercises9. Assumption-Based Reasoning9.1. Introduction9.2. An Assumption-Based Reasoning Framework9.3. Default Reasoning9.4. Abduction9.5. Evidential and Causal Reasoning9.6. Algorithms for Assumption-Based Reasoning9.7. References and Further Reading9.8. Exercises10. Using Uncertain Knowledge10.1. Introduction10.2. Probability10.3. Independence Assumptions10.4. Making Decisions Under Uncertainty10.5. References and Further Reading10.6. Exercises11. Learning11.1. Introduction11.2. Learning as Choosing the Best Representation11.3. Case-Based Reasoning11.4. Learning as Refining the Hypothesis State11.5. Learning Under Uncertainty11.6. Explanation-Based Learning11.7. References and Further Learning11.8. Exercises12. Building Situated Robots12.1. Introduction12.2. Robotic Systems12.3. The Agent Function12.4. Designing Robots12.5. Uses of Agent Models12.6. Robot Architectures12.7. Implementing a Controller12.8. Robots Modeling the World12.9. Reasoning in Situated Robots12.10. References and Further Reading12.11. ExercisesA. GlossaryB. The Prolog Programming LanguageB.1. IntroductionB.2. Interacting with PrologB.3. SyntaxB.4. ArithmeticB.5. Database RelationsB.6. Returning All AnswersB.7. Input and OutputB.8. Controlling SearchC. Some More Implemented SystemsC.1. Bottom-up InterpretersC.2. Top-down InterpretersC.3. A Constraint Satisfaction Problem SolverC.4. Neural Network LearnerC.5. Partial-Order PlannerC.6. Implementing Belief NetworksC.7. Robot ControllerBibliographyIndex