UID:
almahu_9948026283902882
Format:
1 online resource (1035 p.)
Edition:
1st ed.
ISBN:
1-281-14494-0
,
9786611144944
,
0-08-055702-3
Series Statement:
Foundations of artificial intelligence
Content:
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.* Make your computer smarter* Handle qualitative and uncertain information* Improve computational tractability to solve yo
Note:
Description based upon print version of record.
,
Front cover; Handbook of Knowledge Representation; Copyright page; Dedication; Preface; Editors; Contributors; Contents; Part I: General Methods in Knowledge Representation and Reasoning; Chapter 1. Knowledge Representation and Classical Logic; 1.1 Knowledge Representation and Classical Logic; 1.2 Syntax, Semantics and Natural Deduction; 1.3 Automated Theorem Proving; 1.4 Applications of Automated Theorem Provers; 1.5 Suitability of Logic for Knowledge Representation; Acknowledgements; Bibliography; Chapter 2. Satisfiability Solvers; 2.1 Definitions and Notation
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2.2 SAT Solver Technology-Complete Methods2.3 SAT Solver Technology-Incomplete Methods; 2.4 Runtime Variance and Problem Structure; 2.5 Beyond SAT: Quantified Boolean Formulas and Model Counting; Bibliography; Chapter 3. Description Logics; 3.1 Introduction; 3.2 A Basic DL and its Extensions; 3.3 Relationships with other Formalisms; 3.4 Tableau Based Reasoning Techniques; 3.5 Complexity; 3.6 Other Reasoning Techniques; 3.7 DLs in Ontology Language Applications; 3.8 Further Reading; Bibliography; Chapter 4. Constraint Programming; 4.1 Introduction; 4.2 Constraint Propagation; 4.3 Search
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4.4 Tractability4.5 Modeling; 4.6 Soft Constraints and Optimization; 4.7 Constraint Logic Programming; 4.8 Beyond Finite Domains; 4.9 Distributed Constraint Programming; 4.10 Application Areas; 4.11 Conclusions; Bibliography; Chapter 5. Conceptual Graphs; 5.1 From Existential Graphs to Conceptual Graphs; 5.2 Common Logic; 5.3 Reasoning with Graphs; 5.4 Propositions, Situations, and Metalanguage; 5.5 Research Extensions; Bibliography; Chapter 6. Nonmonotonic Reasoning; 6.1 Introduction; 6.2 Default Logic; 6.3 Autoepistemic Logic; 6.4 Circumscription; 6.5 Nonmonotonic Inference Relations
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6.6 Further Issues and ConclusionAcknowledgements; Bibliography; Chapter 7. Answer Sets; 7.1 Introduction; 7.2 Syntax and Semantics of Answer Set Prolog; 7.3 Properties of Logic Programs; 7.4 A Simple Knowledge Base; 7.5 Reasoning in Dynamic Domains; 7.6 Extensions of Answer Set Prolog; 7.7 Conclusion; Acknowledgements; Bibliography; Chapter 8. Belief Revision; 8.1 Introduction; 8.2 Preliminaries; 8.3 The AGM Paradigm; 8.4 Belief Base Change; 8.5 Multiple Belief Change; 8.6 Iterated Revision; 8.7 Non-Prioritized Revision; 8.8 Belief Update; 8.9 Conclusion; Acknowledgements; Bibliography
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Chapter 9. Qualitative Modeling9.1 Introduction; 9.2 Qualitative Mathematics; 9.3 Ontology; 9.4 Causality; 9.5 Compositional Modeling; 9.6 Qualitative States and Qualitative Simulation; 9.7 Qualitative Spatial Reasoning; 9.8 Qualitative Modeling Applications; 9.9 Frontiers and Resources; Bibliography; Chapter 10. Model-based Problem Solving; 10.1 Introduction; 10.2 Tasks; 10.3 Requirements on Modeling; 10.4 Diagnosis; 10.5 Test and Measurement Proposal, Diagnosability Analysis; 10.6 Remedy Proposal; 10.7 Other Tasks; 10.8 State and Challenges; Acknowledgements; Bibliography
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Chapter 11. Bayesian Networks
,
English
Additional Edition:
ISBN 0-444-52211-5
Language:
English
