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Computational Intelligence


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Table of Contents

Preface 1. 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 State 11.5: Learning Under Uncertainty 11.6: Explanation-Based Learning 11.7: References and Further Learning 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 A. Glossary B. The Prolog Programming Language B.1: 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 C. Some More Implemented Systems C.1: Bottom-up Interpreters C.2: Top-down Interpreters C.3: A Constraint Satisfaction Problem Solver C.4: Neural Network Learner C.5: Partial-Order Planner C.6: Implementing Belief Networks C.7: Robot Controller Bibliography Index


"From the title one gets the sense of a fresh approach. Its use of case studies to intertwine theory and practice is excellent."--Jonathan Hodgson, St. Joseph's University

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