Knowledge Representation, which lies at the core of Artificial Intelligence, is concerned with encoding knowledge on computers to enable systems to reason automatically. The Handbook of Knowledge Representation is an up-to-date review of twenty-five key topics in knowledge representation, written by the leaders of each field. This book is an essential resource for students, researchers and practitioners in all areas of Artificial Intelligence.

Key Features

* Make your computer smarter * Handle qualitative and uncertain information * Improve computational tractability to solve your problems easily


Graduate students and researchers in knowledge representation, graduate students and researchers in artificial intelligence, practitioners in artificial intelligence

Table of Contents

Part I: General Methods in Knowledge Representation and Reasoning 1. Knowledge Representation and Classical Logic 2. Satisfiability Solvers 3. Description Logics 4. Constraint Programming 5. Conceptual Graphs 6. Nonmonotonic Reasoning 7. Answer Sets 8. Belief Revision 9. Qualitative Modeling 10. Model-Based Problem Solving 11. Bayesian Networks Part II: Classes of Knowledge and Specialized Representations 12. Temporal Representation and Reasoning 13. Spatial Reasoning 14. Physical Reasoning 15. Reasoning about Knowledge and Belief 16. Situation Calculus 17. Event Calculus 18. Temporal Action Logics 19. Nonmonotonic Causal Logic Part III: Knowledge Representation in Applications 20. Knowledge Representation and Question Answering 21. The Semantic Web: Webizing Knowledge Representation 22. Automated Planning 23. Cognitive Robotics 24. Multi-Agent Systems 25. Knowledge Engineering


No. of pages:
© 2008
Elsevier Science
eBook ISBN:
Print ISBN:

About the editors

Frank van Harmelen

Affiliations and Expertise

Vrije Universiteit Amsterdam, The Netherlands

Vladimir Lifschitz

Affiliations and Expertise

University of Texas at Austin, USA

Bruce Porter

Affiliations and Expertise

University of Texas at Austin, USA