Computational Intelligence

• 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

"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