000			04070nam a2200145 4500
020			_a978019568572 (pb)
040			_cCUS
082			_a006.3 _bPOO/C
100			_aPoole, David
245			_aComputational intelligence / _cDavid Poole, Alan Mackworth and Randy Goebel
260			_aNew York: _bOxford univeristy Press, _c1998.
300			_axvi, 558 p.
505			_a1 Computational Intelligence and Knowledge 1.1 What Is Computational Intelligence? 1.2 Agents in the World 1.3 Representation and Reasoning 1.4 Applications 1.5 Overview 1.6 References and Further Reading 1.7 Exercises 2 A Representation and Reasoning System 2.1 Introduction 2.2 Representation and Reasoning Systems 2.3 Simplifying Assumptions of the Initial RRS 2.4 Datalog 2.5 Semantics 2.6 Questions and Answers 2.7 Proofs 2.8 Extending the Language with Function Symbols 2.9 References and Further Reading 2.10 Exercises 3 Using Definite Knowledge 3.1 Introduction 3.2 Case Study: House Wiring 3.3 Databases and Recursion . 3.4 Verification and Limitations 3.5 Case Study: Representing Abstract Concepts 3.6 Case Study: Representing Regulatory Knowledge 3.7 Applications in Natural Language Processing 3.8 References and Further Reading 3.9 Exercises 4 Searching 4.1 Why Search? 4.2 Graph Searching 4.3 A Generic Searching Algorithm 4.4 Blind Search Strategies 4.5 Heuristic Search 4.6 Refinements to Search Strategies 4.7 Constraint Satisfaction Problems 4.8 References and Further Reading 4.9 Exercises 5 Representing Knowledge 5.1 Introduction 5.2 Defining a Solution 5.3 Choosing a Representation Language 5.4 Mapping from Problem to Representation 5.5 Choosing an Inference Procedure 5.6 References and Further Reading 5.7 Exercises 6 Knowledge Engineering 6.1 Introduction 6.2 Knowledge-Based System Architecture 6.3 Meta-Interpreters 6.4 Querying the User 6.5 Explanation 6.6 Debugging Knowledge Bases 6.7 A Meta-Interpreter with Search 6.8 Unification 6.9 References and Further Reading 6.10 Exercises 7 Beyond Definite Knowledge 7.1 Introduction 7.2 Equality 7.3 Integrity Constraints 7.4 Complete Knowledge Assumption 7.5 Disjunctive Knowledge 7.6 Explicit Quantification 7.7 First-Order Predicate Calculus 7.8 Modal Logic 7.9 References and Further Reading 7.10 Exercises 8 Actions and Planning 8.1 Introduction 8.2 Representations of Actions and Change 8.3 Reasoning with World Representations . 8.4 References and Further Reading 8.5 Exercises 9 Assumption-Based Reasoning 9.1 Introduction 9.2 An Assumption-Based Reasoning Framework 9.3 Default Reasoning 9.4 Abduction 9.5 Evidential and Causal Reasoning 9.6 Algorithms for Assumption-Based Reasoning 9.7 References and. Further Reading 9.8 Exercises 10 Using Uncertain Knowledge 10.1 Introduction 10.2 Probability 10.3 Independence Assumptions 10.4 Making Decisions Under Uncertainty 10.5 References and Further Reading 10.6 Exercises 11 Learning 11.1 Introduction 11.2 Learning as Choosing the Best Representation 11.3 Case-Based Reasoning 11.4 Learning as Refining the Hypothesis Space 11.5 Learning Under Uncertainty 11.6 Explanation-Based Learning 11.7 References and Further Reading 11.8 Exercises 12 Building Situated Robots 12.1 Introduction 12.2 Robotic Systems 12.3 The Agent Function 12.4 Designing Robots 12.5 Uses of Agent Models 12.6 Robot Architectures 12.7 Implementing a Controller 12.8 Robots Modeling the World 12.9 Reasoning in Situated Robots 12.10 References and Further Reading 12.11 Exercises Appendix A Glossary Appendix B The Prolog Programming Language B.l Introduction B.2 Interacting with Prolog B.3 Syntax B.4 Arithmetic B.5 Database Relations B.6 Returning All Answers B.7 Input and Output B.8 Controlling Search Appendix C Some More Implemented Systems C.l Bottom-Up Interpreters C.2 Top-Down Interpreters C.3 Constraint Satisfaction Problem Solver C.4 Neural Network Learner C.5 Partial-Order Planner . . C.6 Implementing Belief Networks C.7 Robot Controller
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