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

Reviews

"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