Handbook of knowledge representation / edited by Bruce Porter, Vladimir Lifschitz and Frank van Harmelen.

CONTENIDO
I General Methods in Knowledge Representation and Reasoning 1
1 Knowledge Representation and Classical Logic 3
1.1 Knowledge Representation and Classical Logic 3
1.2 Syntax, Semantics and Natural Deduction 4
1.3 Automated Theorem Proving 18
1.4 Applications of Automated Theorem Provers 58
1.5 Suitability of Logic for Knowledge Representation 67
2 Satisfiability Solvers 89
2.1 Definitions and Notation 91
2.2 SAT Solver Technology - Complete Methods 92
2.3 SAT Solver Technology - Incomplete Methods 107
2.4 Runtime Variance and Problem Structure 112
2.5 Beyond SAT: Quantified Boolean Formulas and Model Counting 117
3 Description Logics 135
3.1 Introduction 135
3.2 A Basic DL and its Extensions 139
3.3 Relationships with other Formalisms 144
3.4 Tableau Based Reasoning Techniques 146
3.5 Complexity 151
3.6 Other Reasoning Techniques 155
3.7 DLs in Ontology Language Applications 166
4 Constraint Programming 181
4.1 Introduction 181
4.2 Constraint Propagation 182
4.3 Search 184
4.4 Tractability 189
4.5 Modeling 191
4.6 Soft Constraints and Optimization 193
4.7 Constraint Logic Programming 197
4.8 Beyond Finite Domains 199
4.9 Distributed Constraint Programming 201
4.10 Application Areas 202
5 Conceptual Graphs 213
5.1 From Existential Graphs to Conceptual Graphs 213
5.2 Common Logic 217
5.3 Reasoning with Graphs 223
5.4 Propositions, Situations, and Metalanguage 230
5.5 Research Extensions 233
6 Nonmonotonic Reasoning 239
6.1 Introduction 239
6.2 Default Logic 242
6.3 Autoepistemic Logic 252
6.4 Circumscription 260
6.5 Nonmonotonic Inference Relations 267
6.6 Further Issues and Conclusion 272
7 Answer Sets 285
7.1 Introduction 285
7.2 Syntax and Semantics of Answer Set Prolog 286
7.3 Properties of Logic Programs 292
7.4 A Simple Knowledge Base 300
7.5 Reasoning in Dynamic Domains 302
7.6 Extensions of Answer Set Prolog 307
8 Belief Revision 317
8.1 Introduction 317
8.2 Preliminaries 318
8.3 The AGM Paradigm 318
8.4 Belief Base Change 329
8.5 Multiple Belief Change 335
8.6 Iterated Revision 340
8.7 Non-Prioritized Revision 346
8.8 Belief Update 349
9 Qualitative Modeling 361
9.1 Introduction 361
9.2 Qualitative Mathematics 365
9.3 Ontology 371
9.4 Causality 374
9.5 Compositional Modeling 376
9.6 Qualitative States and Qualitative Simulation 379
9.7 Qualitative Spatial Reasoning 381
9.8 Qualitative Modeling Applications 383
9.9 Frontiers and Resources 387
10 Model-based Problem Solving 395
10.1 Introduction 395
10.2 Tasks 398
10.3 Requirements on Modeling 403
10.4 Diagnosis 407
10.5 Test and Measurement Proposal, Diagnosability Analysis 438
10.6 Remedy Proposal 446
10.7 Other Tasks 454
10.8 State and Challenges 458
11 Bayesian Networks 467
11.1 Introduction 467
11.2 Syntax and Semantics of Bayesian Networks 468
11.3 Exact Inference 473
11.4 Approximate Inference 485
11.5 Constructing Bayesian Networks 489
11.6 Causality and Intervention 497
II Classes of Knowledge and Specialized Representations 511
12 Temporal Representation and Reasoning 513
12.1 Temporal Structures 514
12.2 Temporal Language 520
12.3 Temporal Reasoning 528
12.4 Applications 530
12.5 Concluding Remarks 535
13 Qualitative Spatial Representation and Reasoning 551
13.1 Introduction 551
13.2 Aspects of Qualitative Spatial Representation 554
13.3 Spatial Reasoning 572
13.4 Reasoning about Spatial Change 581
13.5 Cognitive Validity 582
13.6 Final Remarks 583
14 Physical Reasoning 597
14.1 Architectures 600
14.2 Domain Theories 602
14.3 Abstraction and Multiple Models 611
14.4 Historical and Bibliographical 614
15 Reasoning about Knowledge and Belief 621
15.1 Introduction 621
15.2 The Possible Worlds Model 622
15.3 Properties of Knowledge 626
15.4 The Knowledge of Groups 628
15.5 Runs and Systems 633
15.6 Adding Time 635
15.7 Knowledge-based Behaviors 637
15.8 Beyond Square One 643
15.9 How to Reason about Knowledge and Belief 644
16 Situation Calculus 649
16.1 Axiomatizations 650
16.2 The Frame, the Ramification and the Qualification Problems 652
16.3 Reiter's Foundational Axioms and Basic Action Theories 661
16.4 Applications 665
16.5 Concluding Remarks 667
17 Event Calculus 671
17.1 Introduction 671
17.2 Versions of the Event Calculus 672
17.3 Relationship to other Formalisms 684
17.4 Default Reasoning 684
17.5 Event Calculus Knowledge Representation 687
17.6 Action Language E 697
17.7 Automated Event Calculus Reasoning 699
17.8 Applications of the Event Calculus 700
18 Temporal Action Logics 709
18.1 Introduction 709
18.2 Basic Concepts 713
18.3 TAL Narratives 716
18.4 The Relation Between the TAL Languages 724
18.5 The TAL Surface Language 725
18.6 The TAL Base Language 728
18.7 Circumscription and TAL 730
18.8 Representing Ramifications in TAL 735
18.9 Representing Qualifications in TAL 737
18.10 Action Expressivity in TAL 742
18.11 Concurrent Actions in TAL 744
18.12 An Application of TAL: TAL planner 747
19 Nonmonotonic Causal Logic 759
19.1 Fundamentals 762
19.2 Strong Equivalence 765
19.3 Completion 766
19.4 Expressiveness 768
19.5 High-Level Action Language C 770
19.6 Relationship to Default Logic 771
19.7 Causal Theories in Higher-Order Classical Logic 772
19.8 A Logic of Universal Causation 773
III Knowledge Representation in Applications 777
20 Knowledge Representation and Question Answering 779
20.1 Introduction 779
20.2 From English to Logical Theories 783
20.3 The COGEX Logic Prover of the LCC QA System 790
20.5 From Natural Language to Relevant Facts in the ASU QA System 803
20.6 Nutcracker - System for Recognizing Textual Entailment 806
20.7 Mueller's Story Understanding System 810
21 The Semantic Web: Webizing Knowledge Representation 821
21.1 Introduction 821
21.2 The Semantic Web Today 823
21.3 Semantic Web KR Language Design 826
21.4 OWL - Defining a Semantic Web KR Language 831
21.5 Semantic Web KR Challenges 836
21.6 Beyond OWL 836
22 Automated Planning 841
22.1 Introduction 841
22.2 The General Framework 843
22.3 Strong Planning under Full Observability 845
22.4 Strong Cyclic Planning under Full Observability 847
22.5 Planning for Temporally Extended Goals under Full Observability 850
22.6 Conformant Planning 857
22.7 Strong Planning under Partial Observability 859
22.8 A Technological Overview 860
23 Cognitive Robotics 869
23.1 Introduction 869
23.2 Knowledge Representation for Cognitive Robots 870
23.3 Reasoning for Cognitive Robots 873
23.4 High-Level Control for Cognitive Robots 876
24 Multi-Agent Systems 887
24.1 Introduction 887
24.2 Representing Rational Cognitive States 888
24.3 Representing the Strategic Structure of a System 909
25 Knowledge Engineering 929
25.1 Introduction 929
25.2 Baseline 929
25.3 Tasks and Problem-Solving Methods 930
25.4 Ontologies 936
25.5 Knowledge Elicitation Techniques 941