Computational intelligence - a logical approach

Preface 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 2.1 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 Functional Symbols 2.9 References and Further Reading 2.10 Exercises 3.1 Introduction 3.2 Case Study: House Wiring 3.3 Discussion 3.5 Case-Study: Repesenting Abstract Concepts 3.6 Applications in Natural Language Processing 3.7 References and Further Reading 3.8 Exercises 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 5.1 Introduction 5.2 Defining a solution 5.3 Choosing a Representation Language 5.4 Mapping a problem to representation 5.5 Choosing an inference procedure 5.6 References and Further Reading 5.7 Exercises 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 7.1 Equality 7.2 Integrity Constraints 7.3 Complete Knowledge Assumption 7.4 Disjunctive Knowledge 7.5 Explicit Quantification 7.6 First-order predicate calculus 7.7 Modal Logic 7.8 References and Further Reading 7.9 Exercises 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 9.1 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 10.1 Introduction 10.2 Probability 10.3 Independence Assumptions 10.4 Making Decisions Under Uncertainty 10.5 References and Further Reading 10.6 Exercises 11.1 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 12.1 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 Modelling the World 12.9 Reasoning in Situated Robots 12.10 References and Further Reading 12.11 Exercises Appendices A Glossary B The Prolog Programming Language B.1 Introduction B.2 Interacting with Prolog B.3 Syntax 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