Elsevier S & T

Handbook of Knowledge Representation

van Harmelen, Frank; Lifschitz, Vladimir; Porter, Bruce

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Table of contents

Cover
Dedicationv
Prefacevii
Editorsxi
Contributorsxiii
Contentsxv
Part I: General Methods in Knowledge Representation and Reasoning1
Chapter 1. Knowledge Representation and Classical Logic3
1.1 Knowledge Representation and Classical Logic3
1.2 Syntax, Semantics and Natural Deduction4
1.3 Automated Theorem Proving18
1.4 Applications of Automated Theorem Provers58
1.5 Suitability of Logic for Knowledge Representation67
Acknowledgements74
Bibliography74
Chapter 2. Satisfiability Solvers89
2.1 Definitions and Notation91
2.2 SAT Solver Technology-Complete Methods92
2.3 SAT Solver Technology-Incomplete Methods107
2.4 Runtime Variance and Problem Structure112
2.5 Beyond SAT: Quantified Boolean Formulas and Model Counting117
Bibliography122
Chapter 3. Description Logics135
3.1 Introduction135
3.2 A Basic DL and its Extensions139
3.3 Relationships with other Formalisms144
3.4 Tableau Based Reasoning Techniques146
3.5 Complexity151
3.6 Other Reasoning Techniques155
3.7 DLs in Ontology Language Applications166
3.8 Further Reading168
Bibliography169
Chapter 4. Constraint Programming181
4.1 Introduction181
4.2 Constraint Propagation182
4.3 Search184
4.4 Tractability189
4.5 Modeling191
4.6 Soft Constraints and Optimization193
4.7 Constraint Logic Programming197
4.8 Beyond Finite Domains199
4.9 Distributed Constraint Programming201
4.10 Application Areas202
4.11 Conclusions203
Bibliography203
Chapter 5. Conceptual Graphs213
5.1 From Existential Graphs to Conceptual Graphs213
5.2 Common Logic217
5.3 Reasoning with Graphs223
5.4 Propositions, Situations, and Metalanguage230
5.5 Research Extensions233
Bibliography235
Chapter 6. Nonmonotonic Reasoning239
6.1 Introduction239
6.2 Default Logic242
6.3 Autoepistemic Logic252
6.4 Circumscription260
6.5 Nonmonotonic Inference Relations267
6.6 Further Issues and Conclusion272
Acknowledgements277
Bibliography277
Chapter 7. Answer Sets285
7.1 Introduction285
7.2 Syntax and Semantics of Answer Set Prolog286
7.3 Properties of Logic Programs292
7.4 A Simple Knowledge Base300
7.5 Reasoning in Dynamic Domains302
7.6 Extensions of Answer Set Prolog307
7.7 Conclusion309
Acknowledgements310
Bibliography310
Chapter 8. Belief Revision317
8.1 Introduction317
8.2 Preliminaries318
8.3 The AGM Paradigm318
8.4 Belief Base Change329
8.5 Multiple Belief Change335
8.6 Iterated Revision340
8.7 Non-Prioritized Revision346
8.8 Belief Update349
8.9 Conclusion352
Acknowledgements353
Bibliography353
Chapter 9. Qualitative Modeling361
9.1 Introduction361
9.2 Qualitative Mathematics365
9.3 Ontology371
9.4 Causality374
9.5 Compositional Modeling376
9.6 Qualitative States and Qualitative Simulation379
9.7 Qualitative Spatial Reasoning381
9.8 Qualitative Modeling Applications383
9.9 Frontiers and Resources387
Bibliography387
Chapter 10. Model-based Problem Solving395
10.1 Introduction395
10.2 Tasks398
10.3 Requirements on Modeling403
10.4 Diagnosis407
10.5 Test and Measurement Proposal, Diagnosability Analysis438
10.6 Remedy Proposal446
10.7 Other Tasks454
10.8 State and Challenges458
Acknowledgements460
Bibliography460
Chapter 11. Bayesian Networks467
11.1 Introduction467
11.2 Syntax and Semantics of Bayesian Networks468
11.3 Exact Inference473
11.4 Approximate Inference485
11.5 Constructing Bayesian Networks489
11.6 Causality and Intervention497
Acknowledgements498
Bibliography499
Part II: Classes of Knowledge and Specialized Representations511
Chapter 12. Temporal Representation and Reasoning513
12.1 Temporal Structures514
12.2 Temporal Language520
12.3 Temporal Reasoning528
12.4 Applications530
12.5 Concluding Remarks535
Acknowledgements535
Bibliography535
Chapter 13. Qualitative Spatial Representation and Reasoning551
13.1 Introduction551
13.2 Aspects of Qualitative Spatial Representation554
13.3 Spatial Reasoning572
13.4 Reasoning about Spatial Change581
13.5 Cognitive Validity582
13.6 Final Remarks583
Acknowledgements584
Bibliography584
Chapter 14. Physical Reasoning597
14.1 Architectures600
14.2 Domain Theories602
14.3 Abstraction and Multiple Models611
14.4 Historical and Bibliographical614
Bibliography618
Chapter 15. Reasoning about Knowledge and Belief621
15.1 Introduction621
15.2 The Possible Worlds Model622
15.3 Properties of Knowledge626
15.4 The Knowledge of Groups628
15.5 Runs and Systems633
15.6 Adding Time635
15.7 Knowledge-based Behaviors637
15.8 Beyond Square One643
15.9 How to Reason about Knowledge and Belief644
Bibliography645
Further reading647
Chapter 16. Situation Calculus649
16.1 Axiomatizations650
16.2 The Frame, the Ramification and the Qualification Problems652
16.3 Reiter's Foundational Axioms and Basic Action Theories661
16.4 Applications665
16.5 Concluding Remarks667
Acknowledgements667
Bibliography667
Chapter 17. Event Calculus671
17.1 Introduction671
17.2 Versions of the Event Calculus672
17.3 Relationship to other Formalisms684
17.4 Default Reasoning684
17.5 Event Calculus Knowledge Representation687
17.6 Action Language E697
17.7 Automated Event Calculus Reasoning699
17.8 Applications of the Event Calculus700
Bibliography701
Chapter 18. Temporal Action Logics709
18.1 Introduction709
18.2 Basic Concepts713
18.3 TAL Narratives716
18.4 The Relation Between the TAL Languages L(ND) and L(FL)724
18.5 The TAL Surface Language L(ND)725
18.6 The TAL Base Language L(FL)728
18.7 Circumscription and TAL730
18.8 Representing Ramifications in TAL735
18.9 Representing Qualifications in TAL737
18.10 Action Expressivity in TAL742
18.11 Concurrent Actions in TAL744
18.12 An Application of TAL: TALplanner747
18.13 Summary752
Acknowledgements752
Bibliography753
Chapter 19. Nonmonotonic Causal Logic759
19.1 Fundamentals762
19.2 Strong Equivalence765
19.3 Completion766
19.4 Expressiveness768
19.5 High-Level Action Language C+770
19.6 Relationship to Default Logic771
19.7 Causal Theories in Higher-Order Classical Logic772
19.8 A Logic of Universal Causation773
Acknowledgement774
Bibliography774
Part III: Knowledge Representation in Applications777
Chapter 20. Knowledge Representation and Question Answering779
20.1 Introduction779
20.2 From English to Logical Theories783
20.3 The COGEX Logic Prover of the LCC QA System790
20.4 Extracting Relevant Facts from Logical Theories and its Use in the DD QA System about Dynamic D792
20.5 From Natural Language to Relevant Facts in the ASU QA System803
20.6 Nutcracker-System for Recognizing Textual Entailment806
20.7 Mueller's Story Understanding System810
20.8 Conclusion813
Acknowledgements815
Bibliography815
Chapter 21. The Semantic Web: Webizing Knowledge Representation821
21.1 Introduction821
21.2 The Semantic Web Today823
21.3 Semantic Web KR Language Design826
21.4 OWL-Defining a Semantic Web KR Language831
21.5 Semantic Web KR Challenges836
21.6 Beyond OWL836
21.7 Conclusion837
Acknowledgements837
Bibliography838
Chapter 22. Automated Planning841
22.1 Introduction841
22.2 The General Framework843
22.3 Strong Planning under Full Observability845
22.4 Strong Cyclic Planning under Full Observability847
22.5 Planning for Temporally Extended Goals under Full Observability850
22.6 Conformant Planning857
22.7 Strong Planning under Partial Observability859
22.8 A Technological Overview860
22.9 Conclusions863
Bibliography864
Chapter 23. Cognitive Robotics869
23.1 Introduction869
23.2 Knowledge Representation for Cognitive Robots870
23.3 Reasoning for Cognitive Robots873
23.4 High-Level Control for Cognitive Robots876
23.5 Conclusion881
Bibliography882
Chapter 24. Multi-Agent Systems887
24.1 Introduction887
24.2 Representing Rational Cognitive States888
24.3 Representing the Strategic Structure of a System909
24.4 Conclusions920
Bibliography920
Chapter 25. Knowledge Engineering929
25.1 Introduction929
25.2 Baseline929
25.3 Tasks and Problem-Solving Methods930
25.4 Ontologies936
25.5 Knowledge Elicitation Techniques941
Bibliography943
Author Index947
Subject Index987

Book details

Vendor Elsevier S & T
SKU 9780444522115
ISBN-13 9780080557021
Author van Harmelen, Frank; Lifschitz, Vladimir; Porter, Bruce
Category Computers
Subject Computer Science

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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 your problems easily

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