Dubois / Wellman / D'Ambrosio Uncertainty in Artificial Intelligence

Proceedings of the Eighth Conference (1992), July 17-19, 1992, Eighth Conference on Uncertainty in Artificial Intelligence, Stanford University
1. Auflage 2014
ISBN: 978-1-4832-8287-9
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 378 Seiten, Web PDF

Uncertainty in Artificial Intelligence: Proceedings of the Eighth Conference (1992) covers the papers presented at the Eighth Conference on Uncertainty in Artificial Intelligence, held at Stanford University on July 17-19, 1992. The book focuses on the processes, methodologies, technologies, and approaches involved in artificial intelligence. The selection first offers information on Relative Evidential Support (RES), modal logics for qualitative possibility and beliefs, and optimizing causal orderings for generating DAGs from data. Discussions focus on reversal, swap, and unclique operators, modal representation of possibility, and beliefs and conditionals. The text then examines structural controllability and observability in influence diagrams, lattice-based graded logic, and dynamic network models for forecasting. The manuscript takes a look at reformulating inference problems through selective conditioning, entropy and belief networks, parallelizing probabilistic inference, and a symbolic approach to reasoning with linguistic quantifiers. The text also ponders on sidestepping the triangulation problem in Bayesian net computations; exploring localization in Bayesian networks for large expert systems; and expressing relational and temporal knowledge in visual probabilistic networks. The selection is a valuable reference for researchers interested in artificial intelligence.

Autoren/Hrsg.

Dubois, Didier J.

Wellman, Michael P.

D'Ambrosio, Bruce

Inhaltsverzeichnis

1;Front Cover;1
2;Uncertainty in Artificial
Intelligence;4
3;Copyright Page;5
4;Table of Contents;8
5;Preface;6
6;Acknowledgments;7
7;Chapter 1. ReS—A Relative Method for Evidential Reasoning;12
7.1;Abstract;12
7.2;1 Introduction;12
7.3;2 Our Point of View;13
7.4;3 An Example;17
7.5;4 Summary;19
7.6;Acknowledgements;19
7.7;References;19
8;Chapter 2. Optimizing Causal Orderings for Generating DAGs from Data;20
8.1;Abstract;20
8.2;1 Introduction;20
8.3;2 Preliminaries;20
8.4;3 The swap operator;22
8.5;4 The reversal operator;23
8.6;5 The cliquereunion operator;24
8.7;6 The unclique operator;25
8.8;7 The algorithm;25
8.9;8 Conclusion;27
8.10;Acknowledgement;27
8.11;References;27
9;Chapter 3. Modal Logics for Qualitative Possibility and Beliefs;28
9.1;Abstract;28
9.2;1 Introduction;28
9.3;2 A Modal Representation of Possibility;29
9.4;3 Beliefs and Conditionals;32
9.5;4 Concluding Remarks;34
9.6;Acknowledgements;35
9.7;References;35
10;Chapter 4. Structural Controllability and Observability in Influence Diagrams;36
10.1;Abstract;36
10.2;1 Introduction;36
10.3;2 Structural Observability in Influence Diagram;38
10.4;3 Structural Controllability in Influence Diagram;41
10.5;4 Conclusions;42
10.6;Acknowledgement;42
10.7;References;42
11;Chapter 5. Lattice-Based Graded Logic: A Multimodal Approach;44
11.1;Abstract;44
11.2;1· INTRODUCTION;44
11.3;2. THE LANGUAGE;45
11.4;3 SEMANTICS;48
11.5;4 RELATED WORK;49
11.6;CONCLUSION AND PERSPECTIVES;50
11.7;References;51
12;Chapter 6. Dynamic Network Models for Forecasting;52
12.1;Abstract;52
12.2;1 INTRODUCTION;52
12.3;2 DYNAMIC NETWORK MODELS;53
12.4;3 BUILDING AND REFINING A DNM;54
12.5;4 FORECASTING: INFERENCE WITH A DNM;55
12.6;5 SPECIAL DNMS;55
12.7;6 NUMERICAL EXAMPLE;56
12.8;7 RELATED WORK;58
12.9;8 CONCLUSION;58
12.10;Acknowledgments;58
12.11;References;58
13;Chapter 7. Reformulating Inference Problems Through Selective Conditioning;60
13.1;Abstract;60
13.2;1 INTRODUCTION;60
13.3;2 RELATED WORK;60
13.4;3 RANDOMIZED APPROXIMATION SCHEMES;61
13.5;4 RAS ALGORITHMS FOR INFERENCE;61
13.6;5 DEPENDENCE VALUE OF BELIEF NETWORKS;61
13.7;6 DEPENDENCE VALUE AND TRACTABILITY;62
13.8;7 PROBLEM REFORMULATION;62
13.9;8 DIRICHLET DISTRIBUTIONS;62
13.10;9 DIRICHLET STOPPING RULES;63
13.11;10 STRUCTURE AND EFFECTS OF PRIOR PROBABILITIES;63
13.12;11 ANALYSIS OF REFORMULATION TRADEOFFS;63
13.13;12 SUMMARY AND CONCLUSIONS;64
13.14;Acknowledgments;64
13.15;References;64
14;Chapter 8. Entropy and Belief Networks;66
14.1;Abstract;66
14.2;1 Introduction;66
14.3;2 Webs;66
14.4;3 Logarithmic Scores;67
14.5;4 An Alternative Model;68
14.6;References;69
15;Chapter 9. Parallelizing Probabilistic Inference Some Early Explorations;70
15.1;Abstract;70
15.2;1 Introduction;70
15.3;2 Background;70
15.4;3 Models;71
15.5;4 Method;73
15.6;5 Results;73
15.7;6 Discussion;75
15.8;7 Summary;77
15.9;References;77
16;Chapter 10. Objection-Based Causal Networks;78
16.1;Abstract;78
16.2;1 INTRODUCTION;78
16.3;2 OBJECTION-BASES STATES OF
BELIEF;78
16.4;3 COMPONENTS OF A CAUSAL NETWORK;80
16.5;4 FROM CAUSAL NETWORKS TO STATES OF BELIEF;83
16.6;5 DISCUSSION;83
16.7;Acknowledgement;83
16.8;References;84
17;Chapter 11. A Symbolic Approach to Reasoning with Linguistic Quantifiers;85
17.1;Abstract;85
17.2;1 INTRODUCTION;85
17.3;2 LATTICES OF LABELS;86
17.4;3 LOCAL PROPAGATION OF INTERVAL-VALUED PROBABILITIES;86
17.5;4 THE QUALITATIVE QUANTIFIED SYLLOGISM;87
17.6;5 ROBUSTNESS ANALYSIS;89
17.7;6 A QUALITATIVE ANALYSIS OF ADAMS' INFERENCE RULES;90
17.8;7 THE GENERALIZED BAYES THEOREM;91
17.9;8 SYMBOLIC CONSTRAINT PROPAGATION;92
17.10;9 CONCLUDING REMARKS;93
17.11;References;93
18;Chapter 12. Possibilistic Assumption based Truth Maintenance System, Validation in a Data Fusion Application;94
18.1;Abstract;94
18.2;1 A DATA FUSION APPLICATION;94
18.3;2 ATMS BACKGROUND;95
18.4;3 POSSIBILISTIC LOGIC;95
18.5;4 POSSIBILISTIC ATMS;96
18.6;5 COUPLING A II-ATMS AND AN
INFERENCE ENGINE;97
18.7;6 THE AGGREGATION PHASE IN SEFIR;97
18.8;7 RESULTS;99
18.9;8 A TEST SCENARIO;100
18.10;9 CONCLUSIONS;102
18.11;Acknowledgements;102
18.12;References;102
19;Chapter 13. An entropy-based learning algorithm of Bayesian conditional trees;103
19.1;Abstract;103
19.2;1 Introduction;103
19.3;2 Learning Conditional Trees;104
19.4;3 Learning Networks with Small Cutset of Root Nodes;105
19.5;4 Conditional trees and Similarity networks;105
19.6;5 Related optimization procedures;107
19.7;6 Summary;108
19.8;References;108
20;Chapter 14. Knowledge integration for conditional probability assessments;109
20.1;Abstract;109
20.2;1 INTRODUCTION;109
20.3;2 SOME PRELIMINARIES;110
20.4;3 COHERENCE OF (P, Q);110
20.5;4 EXTENSION TO MARGINAL DISTRIBUTIONS;111
20.6;5 CONCLUSIONS;113
20.7;REFERENCES;114
21;Chapter 15. Integrating Model Construction and Evaluation;115
21.1;Abstract;115
21.2;1 Introduction;115
21.3;2 Review of ALTERID Language;116
21.4;3 Algorithm MCE;116
21.5;4 Example;119
21.6;5 Implementation;121
21.7;6 Future Directions;121
21.8;References;121
22;Chapter 16. Reasoning With Qualitative Probabilities Can Be Tractable;123
22.1;Abstract;123
22.2;1 Introduction: Infinitesimal
Probabilities, Rankings and Common Sense Reasoning;123
22.3;2 Preliminary Definitions: The Ranking k+;125
22.4;3 Plausible Conclusions: Computing the Z+-rank;125
22.5;4 Belief Change, Soft Evidence, and Imprecise Observations;127
22.6;5 Conclusions;130
22.7;Acknowledgements;131
22.8;References;131
23;Chapter 17. A computational scheme for reasoning in dynamic probabilistic networks;132
23.1;Abstract;132
23.2;1 INTRODUCTION;132
23.3;2 TERMINOLOGY;133
23.4;3 REASONING IN DPNs;134
23.5;4 SUMMARY;139
23.6;Acknowledgements;140
23.7;References;140
24;Chapter 18. The Dynamic of Belief in the transferable belief model and Specialization-Generalization Matrices;141
24.1;Abstract;141
24.2;1. INTRODUCTION;141
24.3;2. THE TRANSFERABLE BELIEF MODEL;141
24.4;3. THE PRINCIPLE OF MINIMAL COMMITMENT;142
24.5;4. THE DYNAMIC OF THE TRANSFERABLE BELIEF MODEL;143
24.6;5. SPECIALIZATIONS;143
24.7;6. DEMPSTER'S RULES IN THE VIEW OF SPECIALIZATIONS;144
24.8;7. ANOTHER CHARACTERIZATION OF DEMPSTERS RULE OF COMBINATION;146
24.9;8. GENERALIZATION TO CONTRACTION OF BELIEFS AND DISJUNCTIVE COMBINATIONS;146
24.10;9. CONCLUSIONS;147
24.11;Acknowledgment;147
24.12;References;147
25;Chapter 19. A NOTE ON THE MEASURE OF DISCORD;149
25.1;Abstract;149
25.2;References;152
26;Chapter 20. Semantics for Probabilistic Inference;153
26.1;Abstract;153
26.2;1 INTRODUCTION;153
26.3;2. DEPARTURES FROM CONVENTIONAL ANALYSIS;153
26.4;3 IMPLICATION AND INDUCTIVE VALIDITY;154
26.5;4 SYNTAX;154
26.6;5 SEMANTICS;154
26.7;6 PROBABILISTIC SOUNDNESS;155
26.8;7 PREMISES;155
26.9;8 NUMBERS OF MODELS;155
26.10;9 SPECIFICITY;156
26.11;10 STRENGTH;156
26.12;11 CONFLICTING EVIDENCE;156
26.13;12 THE GENERAL CASE;156
26.14;13 INCONSISTENCY;157
26.15;14 RELATION TO OTHER WORK;157
26.16;15 CONCLUSION;158
26.17;Acknowledgement;158
26.18;References;158
27;Chapter 21. Some Problems for Convex Bayesians;160
27.1;Abstract;160
27.2;1 CONVEX BAYESIANISM;160
27.3;2 PROBLEMS;160
27.4;3 CONCLUSION;164
27.5;Acknowledgments;165
27.6;References;165
28;Chapter 22. Bayesian Meta-Reasoning: Determining Model Adequacy from Within a Small World;166
28.1;Abstract;166
28.2;1 INTRODUCTION;166
28.3;2 HYPOTHESIS TESTS AND THE BOUNDED BAYESIAN;166
28.4;3 A TEST STATISTIC;167
28.5;4 DETECTING INCORRECT STRUCTURE;168
28.6;5 INCOMPLETE DATA;169
28.7;6 DISCUSSION;169
28.8;Acknowledgments;169
28.9;References;169
29;Chapter 23. The Bounded Bayesian;170
29.1;Abstract;170
29.2;1 INTRODUCTION;170
29.3;2 THE SEQUENTIAL FORECASTING PROBLEM;171
29.4;3 MODEL SEARCH AND REVISION;171
29.5;4. INCORRECT MODELS;175
29.6;A APPENDIX: PROOFS OF RESULTS;175
29.7;Acknowledgments;176
29.8;References;176
30;Chapter 24. Representing Context-Sensitive Knowledge in a Network Formalism: A Preliminary Report;177
30.1;Abstract;177
30.2;1 INTRODUCTION;177
30.3;2 A PARTIAL NETWORK;178
30.4;3 REPRESENTATION OF CONCEPTS;178
30.5;4 STRUCTURE OF KNOWLEDGE BASE;181
30.6;5 INFERENCES SUPPORTED;181
30.7;6 SUPPORTING DECISION MAKING;182
30.8;7 DISCUSSION AND CONCLUSION;183
30.9;Acknowledgments;184
30.10;References;184
31;Chapter 25. A Probabilistic Network of Predicates;185
31.1;Abstract;185
31.2;1 INTRODUCTION;185
31.3;2 EVENT NETWORK;187
31.4;3 SCENARIOS AS EXPLANATIONS;188
31.5;4 INFERENCE ALGORITHM;189
31.6;5 PLAN RECOGNITION;189
31.7;6 IMPRECISE OBSERVATIONS;190
31.8;7 SPECIFICITY OF EXPLANATION;191
31.9;8 CONCLUSION;192
31.10;Acknowledgement;192
31.11;References;192
32;Chapter 26. Representing Heuristic Knowledge in D-S Theory;193
32.1;Abstract;193
32.2;1 INTRODUCTION;193
32.3;2 REPRESENTING HEURISTIC KNOWLEDGE IN D-S THEORY;194
32.4;3 THE RELATION BETWEEN EVIDENTIAL MAPPINGS AND BAYESIAN CONDITIONAL PROBABILITIES;197
32.5;4 CONSTRUCTING COMPLETE EVIDENTIAL MAPPING MATRICES TO PROPAGATE MASS FUNCTIONS FROM AN EVIDENCE SPACE TE TO A HYPOTHESIS SPACE
TH;198
32.6;5 PROPAGATING BELIEFS USING HEURISTIC
KNOWLEDGE;199
32.7;6 CONCLUSION;200
32.8;Acknowledgement;201
32.9;References;201
33;Chapter 27. The Topological Fusion of Bayes Nets;202
33.1;Abstract;202
33.2;1 INTRODUCTION;202
33.3;2 COMPROMISE AND
CONSENSUS;203
33.4;3 TOPOLOGICAL FUSION;204
33.5;4 AN EXAMPLE;208
33.6;5 SUMMARY;209
33.7;References;209
34;Chapter 28. Calculating Uncertainty Intervals From Conditional Convex Sets of
Probabilities;210
34.1;Abstract;210
34.2;1 INTRODUCTION;210
34.3;2 PREVIOUS CONCEPTS AND
PROTOTYPICAL EXAMPLES;211
34.4;3 CONDITIONAL PROBABILITIES
WITH POSSIBILITY VALUES;212
34.5;4 TRANSFORMATION OF A CONDITIONAL PROBABILITY
ON AN INTERVAL;213
34.6;5 CONCLUSIONS;217
34.7;Acknowledgements;217
34.8;References;217
35;Chapter 29. Sensor Validation using Dynamic Belief Networks;218
35.1;Abstract;218
35.2;1 INTRODUCTION;218
35.3;2 THE DOMAIN;219
35.4;3 INCORRECT DATA;220
35.5;4 HANDLING INCORRECT DATA WITHIN
THE BASIC DBN;221
35.6;5 EXPLAINING BAD DATA AS A
DEFECTIVE SENSOR;223
35.7;6 CONCLUSIONS;224
35.8;Acknowledgements;225
35.9;References;225
36;Chapter 30. Empirical Probabilities in Monadic Deductive Databases;226
36.1;Abstract;226
36.2;1 Introduction;226
36.3;2 Empirical Programs;226
36.4;3 Model Theoretic Semantics;227
36.5;4 Query Processing for Consistent
Empirical Programs;230
36.6;5 Related Work;232
36.7;6 Conclusions;233
36.8;Acknowledgements;233
36.9;References;233
37;Chapter 31. aHUGIN: A System Creating Adaptive Causal Probabilistic
Networks;234
37.1;Abstract;234
37.2;1 Introduction;234
37.3;2 Analysis of adaptation;234
37.4;3 Features of aHUGIN;236
37.5;4 Experiments with a HUGIN;237
37.6;References;240
38;Chapter 32. MESA: Maximum Entropy by Simulated Annealing;241
38.1;Abstract;241
38.2;1 INTRODUCTION;241
38.3;2 NOTATION AND COST
FUNCTIONS;242
38.4;3 GENERATING A JOINT
SYNTHETIC SAMPLE;242
38.5;4 MAXIMIZING THE ENTROPY
OF A SYNTHETIC SAMPLE;243
38.6;5 MARGINAL MODELS;244
38.7;6 ALGORITHM FOR MARGINAL
MODELS;244
38.8;7 SUMMARY;246
38.9;Acknowledgements;246
38.10;References;246
38.11;8 APPENDIX;247
39;Chapter 33. Decision Methods for Adaptive Task-Sharing
in Associate Systems;249
39.1;Abstract;249
39.2;1 Introduction;249
39.3;2 Associate Systems;250
39.4;3 The Mars Rover Manager's Associate
(MRMA) system;251
39.5;4 Analysis and Review;253
39.6;5 Implications and Future Work;253
39.7;References;253
40;Chapter 34. Modeling Uncertain Temporal Evolutions
in Model-Based Diagnosis;255
40.1;Abstract;255
40.2;1 INTRODUCTION;255
40.3;2 STOCHASTIC PROCESSES
AND RELIABILITY THEORY;256
40.4;3 DIAGNOSTIC FRAMEWORK;257
40.5;4 DISCUSSION;260
40.6;Acknowledgements;261
40.7;References;261
41;Chapter 35. Guess-And-Verify Heuristics for Reducing Uncertainties in
Expert Classification Systems;263
41.1;Abstract;263
41.2;INTRODUCTION;263
41.3;1. PROBLEM FORMULATION;263
41.4;2. EXACT AND HEURISTIC PROCEDURES;265
41.5;3. EXPERIMENTAL RESULTS AND A
HYBRID HEURISTIC;267
41.6;4. CONCLUSIONS;268
41.7;References;268
42;Chapter 36. R&D Analyst: An Interactive Approach to Normative Decision System Model
Construction;270
42.1;Abstract;270
42.2;1 INTRODUCTION;270
42.3;2 NORMATIVE DECISION SYSTEMS;270
42.4;3 BLACKBOARD ARCHITECTURES;271
42.5;4 AN OVERVIEW OF R&D ANALYST;271
42.6;5 AN OVERVIEW OF R&D ANALYST;274
42.7;6 ADVANCED ISSUES;276
42.8;7 RESEARCH ISSUES;277
42.9;8 CONCLUSIONS;277
42.10;Acknowledgements;277
42.11;References;277
43;Chapter 37. Possibilistic Constraint Satisfaction Problems or
"How to handle soft constraints ?";279
43.1;Abstract;279
43.2;1 Introduction;279
43.3;2 Possibilistic constraint satisfaction
problems;280
43.4;3 A design problem;284
43.5;4 Related works;285
43.6;5 Further researchs;285
43.7;Acknowledgements;285
43.8;References;285
44;Chapter 38. Decision Making Using Probabilistic Inference Methods;287
44.1;Abstract;287
44.2;1 INTRODUCTION;287
44.3;2 MAKING DECISIONS;287
44.4;3 USING GENERAL PROBABILISTIC
INFERENCE ALGORITHMS;288
44.5;4 CLUSTERING ALGORITHM
MODIFICATIONS;290
44.6;5. DYNAMIC PROGRAMMING;292
44.7;6 CONCLUSIONS;293
44.8;7 ACKNOWLEDGEMENTS;294
44.9;8 REFERENCES;294
45;Chapter 39. Conditional Independence in Uncertainty Theories;295
45.1;Abstract;295
45.2;1 INTRODUCTION;295
45.3;2 VALUATION-BASED SYSTEMS;296
45.4;3 INDEPENDENCE AND
CONDITIONAL INDEPENDENCE;299
45.5;4 CONCLUSION;301
45.6;Acknowledgments;301
45.7;References;301
46;Chapter 40. The Nature of the unnormalized Beliefs
encountered in the Transferable Belief Model;303
46.1;Abstract;303
46.2;1. INTRODUCTION;303
46.3;2. THE FRAME OF DISCERNMENT;304
46.4;3. CONDITIONING IN THE
TBM;304
46.5;4. UPDATING
BELIEFS;305
46.6;5. THE EPISTEMIC CONSTRUCT OF
THE FRAME OF DISCERNMENT;307
46.7;6. CONCLUSIONS;307
46.8;Acknowledgments;308
46.9;Bibliography;308
47;Chapter 41. Intuitions about Ordered Beliefs Leading to Probabilistic Models;309
47.1;Abstract;309
47.2;1 INTRODUCTION;309
47.3;2 ASSUMPTIONS FOR
QUALITATIVE PROBABILITIES;310
47.4;3 FROM QUALITATIVE TO
ORDINARY PROBABILITY;311
47.5;4 A NOTE ON SET-VALUED
MODELS;311
47.6;5 CONCLUSIONS;312
47.7;References;313
48;Chapter 42. Expressing Relational and Temporal Knowledge
in Visual Probabilistic Networks;314
48.1;Abstract;314
48.2;1 INTRODUCTION;314
48.3;2 EXPRESSING RELATIONAL
KNOWLEDGE;314
48.4;3 EXPRESSING TEMPORAL
KNOWLEDGE;316
48.5;4 APPLICATION TO COLONOSCOPY;317
48.6;5 CONCLUSIONS;320
48.7;Acknowledgments;320
48.8;References;320
49;Chapter 43. A Fuzzy Logic Approach to Target Tracking;321
49.1;Abstract;321
49.2;I. INTRODUCTION;321
49.3;II. PROBLEM FORMULATION;321
49.4;III. DESIGN OF THE FUZZY TRACKER;321
49.5;IV. SIMULATION RESULTS;323
49.6;V. CONCLUDING REMARKS;324
49.7;References;324
50;Chapter 44. Towards Precision of Probabilistic Bounds Propagation;326
50.1;Abstract;326
50.2;1 Introduction;326
50.3;2 The DUCK Calculus for Uncertain
Inference;327
50.4;3 Precise Bounds for Probabilistic
Entailment;329
50.5;4 Summary and Outlook;332
50.6;References;333
51;Chapter 45. An Algorithm for Deciding if a Set of Observed Independencies
Has a Causal Explanation;334
51.1;Abstract;334
51.2;1 Introduction;334
51.3;2 The DAG Construction Algorithm;335
51.4;3 Correctness;336
51.5;4 Complexity Analysis;337
51.6;5 Extensions and Improvements;337
51.7;Acknowledgement;338
51.8;Appendix: Proof of Lemmas;338
51.9;References;340
52;Chapter 46. Generalizing Jeffrey Conditionalization;342
52.1;Abstract;342
52.2;1 INTRODUCTION;342
52.3;2 GENERALIZED PROBABILITY
KINEMATICS;342
52.4;3 BOUNDING POSTERIORS BY
TWO-MONOTONE CAPACITIES;343
52.5;4 REVISING A PRIOR LOWER
PROBABILITY;344
52.6;5 OTHER APPROACHES;344
52.7;References;346
53;Chapter 47. INTERVAL STRUCTURE:
A Framework for Representing Uncertain Information;347
53.1;Abstract;347
53.2;1 INTRODUCTION;347
53.3;2 INTERVAL STRUCTURE INDUCED BY A
COMPATIBILITY RELATION;348
53.4;3 REPRESENTATIONS OF
UNCERTAINTY BY INTERVAL STRUCTURES;349
53.5;4 KNOWLEDGE SYNTHESIS
USING INTERVAL STRUCTURE;351
53.6;5 CONCLUSION;353
53.7;References;354
54;Chapter 48. Exploring Localization In Bayesian Networks For
Large Expert Systems;355
54.1;Abstract;355
54.2;1 LOCALIZATION;355
54.3;2 EXPLORE LOCALIZATION IN
BAYESIAN NETS;356
54.4;3 MULTIPLY SECTIONED
BAYESIAN NETS;358
54.5;4 TECHNICAL ISSUES;360
54.6;5 CONCLUSION;361
54.7;Acknowledgements;362
54.8;References;362
55;Chapter 49. A Decision Calculus for Belief Functions
in Valuation-Based Systems;363
55.1;Abstract;363
55.2;0. INTRODUCTION;363
55.3;1. VALUATION-BASED SYSTEM FOR
DECISION PROBLEMS;364
55.4;2. DECISION ANALYSIS USING
BELIEF FUNCTIONS IN VBS;365
55.5;3. CONCLUSIONS;369
55.6;Acknowledgements;369
55.7;References;369
55.8;APPENDIX;369
56;Chapter 50. Sidestepping the Triangulation Problem
in Bayesian Net Computations;371
56.1;Abstract;371
56.2;1 INTRODUCTION;371
56.3;2 PREREQUISITE;372
56.4;3 DECOMPOSITION;373
56.5;4 PARALLEL REDUCTION;374
56.6;5 SERIAL REDUCTION;375
56.7;6 COMPONENT TREES;376
56.8;7 COMPONENT TREE PROPAGATION;377
56.9;8 CONCLUSIONS;378
56.10;Acknowledgement;378
56.11;References;378
57;Author Index;379