D'Ambrosio / Smets / Bonissone Uncertainty in Artificial Intelligence

Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence, UCLA, at Los Angeles, July 13-15, 1991
1. Auflage 2014
ISBN: 978-1-4832-9856-6
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 445 Seiten, Web PDF

Uncertainty Proceedings 1991

Autoren/Hrsg.

D'Ambrosio, Bruce

Smets, Philippe

Bonissone, Piero Patrone

Inhaltsverzeichnis

1;Front Cover;1
2;Uncertainty in Artificial Intelligence;4
3;Copyright Page;5
4;Table of Contents;8
5;Preface;6
6;Chapter 1. ARCO1: An Application of Belief Networks to the Oil Market;12
6.1;Abstract;12
6.2;1 Introduction;12
6.3;2 Domain Specifics;12
6.4;3 Model Variables;13
6.5;4 Scenarios;14
6.6;5 Forecasts;15
6.7;6 Conclusions;16
6.8;7 Acknowledgements;17
6.9;8 References;17
7;Chapter 2. "Conditional Inter-Causally Independent" node distributions, a property of "noisy-or" models;20
7.1;Abstract;20
7.2;1 EVIDENCE NODES THAT ARE COMMON TO MULTIPLE PARENTS;20
7.3;2 CONSTRUCTIVE SOLUTION OF THE BINARY VARIABLE INTER-CAUSAL DEPENDENCY;25
7.4;3 DISCUSSION;27
7.5;Acknowledgements;27
7.6;References;27
8;Chapter 3. Combining Multiple-valued Logics in Modular Expert Systems;28
8.1;Abstract;28
8.2;1 INTRODUCTION;28
8.3;2 ENTAILMENT SYSTEMS;29
8.4;3 A CLASS OF MULTIPLE-VALUED LOGICS FOR THE UNCERTAINTY MANAGEMENT IN RULE-BASED EXPERT SYSTEMS;30
8.5;4 INFERENCE PRESERVING MAPS BETWEEN MV-LOGICS;31
8.6;5 CONCLUSIONS AND FUTURE WORK;35
8.7;Acknowledgements;36
8.8;References;36
9;Chapter 4. Constraint Propagation with Imprecise Conditional Probabilities;37
9.1;Abstract;37
9.2;1 INTRODUCTION;37
9.3;2 STATEMENT OF THE PROBLEM;38
9.4;3 A LINEAR PROGRAMMING METHOD;39
9.5;4 GENERALIZED BAYES' THEOREM;39
9.6;5 LOCAL INFERENCE RULES;40
9.7;6 A CONSTRAINT PROPAGATION BASED ON INFERENCE RULES;41
9.8;7 AN EXAMPLE;42
9.9;8 CONJUNCTION AND DISJUNCTION;42
9.10;9 INDEPENDENCE ASSUMPTIONS;43
9.11;10 CONCLUSION;44
9.12;Acknowledgements;45
9.13;References;45
10;Chapter 5. BAYESIAN NETWORKS APPLIED TO THERAPY MONITORING;46
10.1;Abstract;46
10.2;1. INTRODUCTION;46
10.3;2. HIGH-LEVEL VIEW OF THE MODEL;47
10.4;3. INFERENCE;48
10.5;4. COMPUTING THE INFERENCES VIA STOCHASTIC SIMULATION;49
10.6;5. SPECIFIC MODEL FOR CYTOTOXIC CHEMOTHERAPY MONITORING IN BREAST CANCER;49
10.7;7. CONCLUSIONS;52
10.8;Acknowledgements;53
10.9;References;53
11;Chapter 6. Some Properties of Plausible Reasoning;55
11.1;Abstract;55
11.2;1 INTRODUCTION;55
11.3;2 NOTATION;56
11.4;3 THEORY;57
11.5;4 EXAMPLES;59
11.6;5 CONCLUSION;60
11.7;References;61
12;Chapter 7. Theory Refinement on Bayesian Networks;63
12.1;Abstract;63
12.2;1 Introduction;63
12.3;2 Bayesian Networks;64
12.4;3 Partial Bayesian networks;65
12.5;4 Representing alternative Bayesian networks;66
12.6;5 Theory Refinement;67
12.7;6 Extensions;69
12.8;7 Conclusion;70
12.9;Acknowledgements;70
12.10;References;70
13;Chapter 8. COMBINATION OF UPPER AND LOWER PROBABILITIES;72
13.1;Abstract;72
13.2;1 INTRODUCTION;72
13.3;2 'A PRIORI' INFORMATION;73
13.4;3 EVIDENTIAL INFORMATION;74
13.5;4 COMBINATION OF 'A PRIORI AND EVIDENTIAL INFORMATION;76
13.6;Acknowledgments;79
13.7;References;79
14;Chapter 9. A Probabilistic Analysis of Marker-Passing Techniques for Plan-Recognition;80
14.1;Abstract;80
14.2;1 Introduction;80
14.3;2 Probabilistic Schema Evaluation;81
14.4;3 Probabilistic Schema Selection;81
14.5;4 Path Calculations;84
14.6;5 Results;86
14.7;Acknowledgements;87
14.8;References;87
15;Chapter 10. Symbolic Probabilistic Inference with Continuous Variables;88
15.1;Abstract;88
15.2;1 Introduction;88
15.3;2 Overview of the SPI Algorithm;89
15.4;3 The SPI with Continuous Variables Algorithm;90
15.5;4 Conclusion;92
15.6;References;92
16;Chapter 11. Symbolic Probabilistic Inference with Evidence Potential;93
16.1;Abstract;93
16.2;1 Introduction;93
16.3;2 Evidence Potential Algorithm;94
16.4;3 Symbolic Inference with Evidence Potential;94
16.5;4 Examples;95
16.6;5 Conclusion;96
16.7;References;96
17;Chapter 12. A Bayesian Method for Constructing Bayesian Belief Networks from Databases;97
17.1;Abstract;97
17.2;1 INTRODUCTION;97
17.3;2 METHODS;98
17.4;3 PRELIMINARY RESULTS;103
17.5;4 SUMMARY OF THE LEARNING METHOD AND RELATED WORK;103
17.6;Acknowledgements;104
17.7;References;104
18;Chapter 13. Local Expression Languages for Probabilistic Dependence: a preliminary report;106
18.1;Abstract;106
18.2;1 Introduction;106
18.3;2 Overview of SPI;106
18.4;3 Local Expression Languages for Probabilistic Knowledge;108
18.5;4 Discussion;112
18.6;5 Conclusion;112
18.7;Acknowledgements;113
18.8;References;113
19;Chapter 14. Symbolic Decision Theory and Autonomous Systems;114
19.1;Abstract;114
19.2;1 INTRODUCTION;114
19.3;2 SYMBOLIC DECISION MAKING UNDER UNCERTAINTY;115
19.4;3 AUTONOMOUS DECISION MAKING UNDER UNCERTAINTY;118
19.5;Acknowledgements;121
19.6;References;121
20;Chapter 15. A REASON MAINTENANCE SYSTEM DEALING WITH VAGUE DATA;122
20.1;Abstract;122
20.2;INTRODUCTION;122
20.3;MANY-VALUED LOGICS AND RESOLUTION;122
20.4;DEFINITION OF A FUZZY TRUTH MAINTENANCE SYSTEM;124
20.5;CONCLUSION;127
20.6;Acknowledgements;127
20.7;References;127
21;Chapter 16. Advances in Probabilistic Reasoning;129
21.1;Abstract;129
21.2;1 Introduction;129
21.3;2 Representation and Inference;129
21.4;3 Knowledge Acquisition/Representation;133
21.5;4 Generalized Similarity Networks;135
21.6;5 Summary;136
21.7;References;137
22;Chapter 17. Probability Estimation in face of Irrelevant Information;138
22.1;Abstract;138
22.2;1 INTRODUCTION;138
22.3;2 THE UNDERLYING MODEL;139
22.4;3 THE ESTIMATION PROBLEM;140
22.5;4 JUSTIFICATION AND EXTENSIONS;142
22.6;5 COMPARISON TO OTHER WORK;143
22.7;6 CONCLUSION;144
22.8;Acknowledgments;144
22.9;References;144
23;Chapter 18. An Approximate Nonmyopic Computation for Value of Information;146
23.1;Abstract;146
23.2;1 INTRODUCTION;146
23.3;2 VALUE-OF-INFORMATION COMPUTATIONS FOR DIAGNOSIS;146
23.4;3 MYOPIC ANALYSIS;147
23.5;4 NONMYOPIC ANALYSIS;149
23.6;5 VALUE OF INFORMATION FOR A SUBSET OF EVIDENCE;149
23.7;6 RELAXATION OF THE ASSUMPTIONS;150
23.8;7 SUMMARY AND CONCLUSIONS;152
23.9;Acknowledgments;152
23.10;References;152
24;Chapter 19. Search-based Methods to Bound Diagnostic Probabilities in Very Large Belief Nets;153
24.1;Abstract;153
24.2;1 INTRODUCTION;153
24.3;2 QMR AND INTERNIST;154
24.4;3 QMR-BN: A PROBABILISTIC INTERPRETATION OF QMR;154
24.5;4 INFERENCE ALGORITHMS;155
24.6;5 NOTATION;156
24.7;6 RELATIVE PROBABILITY AND MARGINAL EXPLANATORY POWER;156
24.8;7 NEGATIVE PRODUCT SYNERGY AND THE MEP THEOREM;156
24.9;8 BOUNDS ON THE PROBABILITY OF EXTENSIONS;157
24.10;9 SEARCH METHOD;158
24.11;10 OBTAINING ABSOLUTE PROBABILITIES;158
24.12;11 PERFORMANCE OF TOPN;159
24.13;CONCLUSIONS;160
24.14;Acknowledgements;160
24.15;References;160
25;Chapter 20. Chapter Time-Dependent Utility and Action Under Uncertainty;162
25.1;Abstract;162
25.2;1 INTRODUCTION;162
25.3;2 A LIMITED REASONER;162
25.4;3 TIME-DEPENDENT UTILITY;164
25.5;4 PROTOS IN ACTION;166
25.6;5 SUMMARY;168
25.7;Acknowledgments;169
25.8;References;169
26;Chapter 21. Non-monotonic Reasoning and the Reversibility of Belief Change;170
26.1;Abstract;170
26.2;1 INTRODUCTION;170
26.3;2 BELIEF CHANGE AND INFERENCE;170
26.4;3 SEMANTICS FOR BELIEF CHANGE;171
26.5;4 ITERATED BELIEF CHANGE AND REVERSIBILITY;172
26.6;5 DISCUSSION;174
26.7;Acknowledgements;174
26.8;References;174
27;Chapter 22. Belief and Surprise - A Belief-Function Formulation;176
27.1;Abstract;176
27.2;1 INTRODUCTION;176
27.3;2 BELIEF FUNCTIONS AS A GENERAL FORMALIZATION MECHANISM;178
27.4;3 A CASE STUDY;181
27.5;4 DISCUSSION;182
27.6;5 CONCLUSION;183
27.7;Acknowledgements;183
27.8;Appendix - logical formulas and subsets of ;183
27.9;References;184
28;Chapter 23. Evidential Reasoning in a Categorial Perspective: Conjunction and Disjunction of Belief Functions;185
28.1;Abstract;185
28.2;0 INTRODUCTION;185
28.3;1 FROM THE DYNAMICS OF BELIEFS TO CATEGORIES OR ... VICE VERSA;186
28.4;2 CATEGORIES OF "BELIEFS";187
28.5;3 DISJUNCTIONS AND CONJUNCTIONS;189
28.6;4 COPRODUCTS AND CONJUNCTIONS;189
28.7;5 PRODUCTS AND DISJUNCTIONS;190
28.8;6 SEPARABLE BELIEF FUNCTIONS;191
28.9;7 CONCLUSIONS;191
28.10;Acknowledgments;192
28.11;References;192
29;Chapter 24. Reasoning with Mass Distributions;193
29.1;Abstract;193
29.2;1 INTRODUCTION;193
29.3;2 REPRESENTING KNOWLEDGE WITH MASS DISTRIBUTIONS;193
29.4;3 THE CONCEPT OF SPECIALIZATION;195
29.5;4 SPECIALIZATION MATRICES;196
29.6;5 CONCLUSIONS;198
30;Chapter 25. A Logic of Graded Possibility and Certainty Coping with Partial Inconsistency;199
30.1;ABSTRACT;199
30.2;1 INTRODUCTION;199
30.3;2 POSSIBILISTIC LOGIC : LANGUAGE AND SEMANTICS;200
30.4;3 AUTOMATED DEDUCTION IN POSSIBILISTIC LOGIC;203
30.5;CONCLUSION;206
30.6;Acknowledgements;206
30.7;References;206
31;Chapter 26. Conflict and Surprise: Heuristics for Model Revision;208
31.1;Abstract;208
31.2;1 INTRODUCTION;208
31.3;2 BACKGROUND;208
31.4;3 THEORETICAL FRAMEWORK;210
31.5;4 REBUTTALS;212
31.6;5 RARE CASES;214
31.7;6 DISCUSSION;214
31.8;Acknowledgements;215
31.9;References;215
32;Chapter 27. Reasoning under Uncertainty: Some Monte Carlo Results;216
32.1;Abstract;216
32.2;1 INTRODUCTION;216
32.3;2 METHOD;216
32.4;3 RESULTS;217
32.5;4 DISCUSSION;221
32.6;References;222
33;Chapter 28. Representation Requirements for Supporting Decision Model Formulation;223
33.1;Abstract;223
33.2;1 Introduction;223
33.3;2 An Example;224
33.4;3 The Decision Making Process;224
33.5;4 Summary of Inference Patterns and Representation Requirements;226
33.6;5 A Representation Design;227
33.7;6 Supporting General Inferences;228
33.8;7 Related Work;229
33.9;8 Discussion and Conclusion;229
33.10;Acknowledgments;230
33.11;References;230
34;Chapter 29. A Language for Planning with Statistics;231
34.1;Abstract;231
34.2;1 INTRODUCTION;231
34.3;2 KNOWLEDGE REPRESENTATION;232
34.4;3 INFERENCE;233
34.5;4 PLANNING;235
34.6;5 CONCLUSION;237
34.7;Acknowledgments;238
34.8;References;238
35;Chapter 30. A Modification to Evidential Probability;239
35.1;Abstract;239
35.2;1 Overview of the Problem;239
35.3;2 The Proposed Solution;240
35.4;3 Conclusions;242
35.5;Acknowledgments;242
35.6;References;242
36;Chapter 31. Investigation of Variances in Belief Networks;243
36.1;Abstract;243
36.2;1 INTRODUCTION;243
36.3;2 PRELIMINARY ASSUMPTIONS;245
36.4;3 DETERMINING THE VARIANCES IN INFERRED PROBABILITIES;246
36.5;4 OBTAINING AN UPPERBOUND FOR THE PRIOR VARIANCES;249
36.6;5 FUTURE RESEARCH;252
36.7;References;252
37;Chapter 32. A Sensitivity Analysis of Pathfinder: A Follow-up Study;253
37.1;Abstract;253
37.2;1 INTRODUCTION;253
37.3;2 DETAILS OF THE ANALYSIS;254
37.4;3 THE INITIAL STUDY;254
37.5;4 THE FOLLOW-UP STUDY;255
37.6;5 CONCLUSIONS;257
37.7;Acknowledgments;259
37.8;References;259
38;Chapter 33. Non-monotonic Negation in Probabilistic Deductive Databases;260
38.1;Abstract;260
38.2;1 Introduction;260
38.3;2 Syntax and Uses of General Probabilistic Logic Programs;261
38.4;3 Background: Fixpoint Theory for Pf-programs;262
38.5;4 Stability of Formula Functions;263
38.6;5 Stable Classes of Formula Functions;264
38.7;6 Discussion;265
38.8;7 Conclusions;266
38.9;Acknowledgements;266
38.10;References;266
39;Chapter 34. Management of Uncertainty in the Multi-Level Monitoring and Diagnosis of the Time of Flight Scintillation Array;268
39.1;Abstract;268
39.2;1 INTRODUCTION;268
39.3;2 BACKGROUND LITERATURE;269
39.4;3 TIME OF FLIGHT SCINTILLATION ARRAY;269
39.5;4 SYSTEM ARCHITECTURE;269
39.6;5 MANAGEMENT OFUNCERTAINTY AT THE MONITORING LEVEL;270
39.7;6 MANAGEMENT OF UNCERTAINTY AT THE STRUCTURAL REASONING LEVEL;271
39.8;7 MANAGEMENT OF UNCERTAINTY AT THE BEHAVIORAL REASONING LEVEL;271
39.9;8 IMPLEMENTATION;272
39.10;9 SUMMARY;272
39.11;Acknowledgements;273
39.12;References;273
40;Chapter 35. Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm;275
40.1;Abstract;275
40.2;1 INTRODUCTION;275
40.3;2 PROBABILISTIC NETWORKS;276
40.4;3 MAXIMUM LIKELIHOOD ESTIMATION;277
40.5;4 THE STOCHASTIC EM-ALGORITHM;278
40.6;5 DISCUSSION;280
40.7;Acknowledgements;280
40.8;References;280
41;Chapter 36. Representing Bayesian Networks within Probabilistic Horn Abduction;282
41.1;Abstract;282
41.2;1 Introduction;282
41.3;2 Probabilistic Horn Abduction;282
41.4;3 Representing Bayesian networks;284
41.5;4 Best-first abduction;286
41.6;5 Causation;287
41.7;6 Comparison with Other Systems;287
41.8;7 Conclusion;287
41.9;Acknowledgements;289
41.10;References;289
42;Chapter 37. DYNAMIC NETWORK UPDATING TECHNIQUES FOR DIAGNOSTIC REASONING;290
42.1;Abstract;290
42.2;1 INTRODUCTION;290
42.3;2 DYNAMICS OF DIAGNOSTIC REASONING UNDER UNCERTAINTY;291
42.4;3 SYSTEM ARCHITECTURE;291
42.5;4 MODEL CONSTRUCTION HEURISTICS;292
42.6;5 MODEL UPDATING;294
42.7;6 CONCLUSIONS;297
42.8;References;297
43;Chapter 38. High Level Path Planning with Uncertainty;298
43.1;Abstract;298
43.2;1 INTRODUCTION;298
43.3;2 U–GRAPH;299
43.4;3 PATH PLANNING WITH UNCERTAINTY;299
43.5;4 A FORMAL DEFINITION OF PATH PLANNING;300
43.6;5 RELATED WORK;304
43.7;6 CONCLUSION AND FUTURE WORK;305
43.8;Acknowledgements;305
43.9;References;305
44;Chapter 39. Formal Model of Uncertainty for Possibilistic Rules;306
44.1;OVERVIEW;306
44.2;1 POSSIBILITY DISTRIBUTIONS AND MEASURES;306
44.3;2 INFORMATION FUNCTIONS IN POSSIBILITY THEORY;307
44.4;3 DESIGN OF CONTINUOUS POSSIBILITY INFORMATION;308
44.5;4 PROPERTIES OF CONTINUOUS INFORMATION MEASURES;308
44.6;5 PRINCIPLE OF MAXIMUM UNCERTAINTY;309
44.7;REFERENCES;309
45;Chapter 40. Deliberation and its Role in the Formation of Intentions*;311
45.1;Abstract;311
45.2;1 INTRODUCTION;311
45.3;2 OVERVIEW;312
45.4;3 POSSIBLE WORLDS MODEL;312
45.5;4 DECISION TREES AND GOAL WORLDS;315
45.6;5 DELIBERATION AND INTENTIONS;316
45.7;6 CONCLUSIONS;317
45.8;References;318
46;Chapter 41. Handling Uncertainty during Plan Recognitionin Task-Oriented Consultation Systems;319
46.1;Abstract;319
46.2;1 INTRODUCTION;319
46.3;2 THE INFERENCE MECHANISM;320
46.4;3 THE PROBABILITY OF AN INTERPRETATION OF THE DISCOURSE;321
46.5;4 STRENGTH OF INFERENCES;323
46.6;5 INFORMATION CONTENT AND ITS USE;324
46.7;6 EXAMPLES;325
46.8;7 CONCLUSIONS;326
46.9;Acknowledgments;326
46.10;References;326
47;Chapter 42. TRUTH AS UTILITY: A CONCEPTUAL SYNTHESIS;327
47.1;Abstract;327
47.2;1 Introduction;327
47.3;2 Possible Worlds and Desirabilities;328
47.4;3 Desirability and Preference;329
47.5;4 Combination of Preference Functions;331
47.6;5 Possibility and Necessity;331
47.7;6 Preference, Similarity, and Fuzzy Logic;332
47.8;AckNowledgements;333
47.9;References;333
48;Chapter 43. PULCINELLAA General Tool for Propagating Uncertainty in Valuation Networks;334
48.1;Abstract;334
48.2;1. INTRODUCTION;334
48.3;2. THEORETICAL BACKGROUND;335
48.4;3. PULCINELLA;336
48.5;4. EXAMPLES;338
48.6;5. DISCUSSION;340
48.7;6. CONCLUSIONS;341
48.8;Acknowledgements;342
48.9;References;342
49;Chapter 44. Structuring Bodies of Evidence;343
49.1;Abstract;343
49.2;1 INTRODUCTION;343
49.3;2 BASIC NOTIONS IN EVIDENCE THEORY;343
49.4;3 PROPOSAL OF STRUCTURES;344
49.5;4 DEMPSTER RULE OF COMBINATION;347
49.6;5 LOCAL PROPAGATION OF INFORMATION;348
49.7;6 CONCLUSION;349
49.8;Acknowledgements;349
49.9;References;349
50;Chapter 45. On the Generation of Alternative Explanations with Implications for Belief Revision;350
50.1;Abstract;350
50.2;1 INTRODUCTION;350
50.3;2 CONSTRAINT SYSTEMS;351
50.4;3 GENERATING ALTERNATIVE EXPLANATIONS;352
50.5;4 BAYESIAN NETWORKS;355
50.6;5 DISCUSSION;357
50.7;Acknowledgments;358
50.8;References;358
51;Chapter 46. Completing Knowledge by Competing Hierarchies;359
51.1;Abstract;359
51.2;1 Introduction;359
51.3;2 The knowledge base;359
51.4;3· The control strategy;360
51.5;4. The application to a multi-hierarchical knowledge base;362
51.6;5. Discussion;363
51.7;Acknowledgements;363
51.8;References;363
52;Chapter 47. A Graph-Based Inference Method for Conditional Independence;364
52.1;Abstract;364
52.2;1. INTRODUCTION;364
52.3;2. NOTATION AND BASIC CONCEPTS;364
52.4;3. MULTIPLE UNDIRECTED GRAPHS;365
52.5;4. GRAPHICAL REPRESENTATION OF THE GRAPHOID AXIOMS;366
52.6;5. EXTENSIONS TO THE GRAPHICAL OPERATIONS;367
52.7;6. EXAMPLES;367
52.8;7. CONCLUSIONS;370
52.9;Acknowledgements;370
52.10;References;370
53;Chapter 48. A Fusion Algorithm for Solving Bayesian Decision Problems;372
53.1;Abstract;372
53.2;1 INTRODUCTION;372
53.3;2 A DIABETES DIAGNOSIS PROBLEM;372
53.4;3 VALUATION-BASED SYSTEM REPRESENTATION;373
53.5;4 SOLVING A VBS;376
53.6;5 A FUSION ALGORITHM;377
53.7;6 CONCLUSIONS;378
53.8;Acknowledgements;380
53.9;References;380
54;Chapter 49. Algorithms for Irrelevance-Based Partial MAPs;381
54.1;Abstract;381
54.2;1 INTRODUCTION;381
54.3;2 IB-MAP ALGORITHM;384
54.4;3 d-IB MAP ALGORITHM;387
54.5;4 FUTURE WORK;387
54.6;5 SUMMARY;388
54.7;Acknowledgements;388
54.8;References;388
55;Chapter 50. About Updating;389
55.1;Abstract;389
55.2;1. CONDITIONING RULES FOR BELIEF FUNCTIONS;389
55.3;2. THE SCENARIO: THE VOTING INTENTIONS STUDY;392
55.4;3. CONDITIONING;392
55.5;4. BELIEFS INDUCED BY THE PROPORTIONS;395
55.6;5. CONCLUSIONS;396
55.7;Acknowledgements;396
55.8;Bibliography;396
56;Chapter 51. Compressed Constraints in Probabilistic Logic and Their Revision;397
56.1;Abstract;397
56.2;1. PROLIFERATION OF WORLDS;397
56.3;2. OVERVIEW AND EXAMPLE;398
56.4;3. COMPRESSION USING KNOWLEDGE AND USING SEARCH;398
56.5;4. EXPRESSING THE CONSTRAINTS;400
56.6;5. REVISION WITH CONDITIONALS;400
56.7;6. AN EXAMPLE OF REVISION;401
56.8;7. REVISION USING POSTERIORS;402
56.9;8· CONCLUSIONS;402
56.10;Literature Cited;402
57;Chapter 52. Detecting Causal Relations in the Presence of Unmeasured Variables;403
57.1;Abstract;403
57.2;1 Introduction;403
57.3;2 Results;403
57.4;3 Can Theorem 3 Be Strengthened?;405
57.5;4 Appendix;406
57.6;Acknowledgements;407
57.7;References;408
58;Chapter 53. A Method for Integrating Utility Analysis into an Expert System for Design Evaluation under Uncertainty ;409
58.1;Abstract;409
58.2;1. INTRODUCTION;409
58.3;2. INTEGRATION OF USER-DEFINED EVALUATION FUNCTION INTO EXPERT SYSTEM;410
58.4;3. EXAMPLE: AUTOMOTIVE BUMPER MATERIAL SELECTION KBS;413
58.5;4. CONCLUSIONS;415
58.6;Acknowledgment;415
58.7;References;415
59;Chapter 54. From Relational Databases to Belief Networks;417
59.1;Abstract;417
59.2;1 INTRODUCTION;417
59.3;2 RELATIONAL DATABASES;417
59.4;3 BELIEF NETWORKS;419
59.5;4 INITIAL DISTRIBUTIONS;422
59.6;5 CONCLUSIONS;423
59.7;Acknowledgement;424
59.8;References;424
60;Chapter 55. A Monte-Carlo Algorithm for Dempster-Shafer Belief;425
60.1;Abstract;425
60.2;1 INTRODUCTION;425
60.3;2 THE MONTE-CARLO ALGORITHM;425
60.4;3 COMPUTATION TIME;426
60.5;4 EXPERIMENTAL RESULTS;426
60.6;5 THE GENERALISED ALGORITHM;427
60.7;6 DISCUSSION;427
60.8;Acknowledgements;428
60.9;References;428
61;Chapter 56. Compatibility of Quantitative and Qualitative Representations of Belief;429
61.1;Abstract;429
61.2;1 INTRODUCTION;429
61.3;2 QUANTITATIVE BELIEF MEASURES;430
61.4;3 PREFERENCE RELATIONS VERSUS QUANTITATIVE BELIEF MEASURES;431
61.5;4 CONCLUSION;434
61.6;Acknowledgements;435
61.7;References;435
62;Chapter 57. An Efficient Implementation of Belief Function Propagation;436
62.1;Abstract;436
62.2;1 INTRODUCTION;436
62.3;2 SOME BASIC CONCEPTS ABOUT BELIEF FUNCTION NETWORKS;436
62.4;3 BELIEF FUNCTION PROPAGATION USING LOCAL COMPUTATION;437
62.5;4 A More Efficient Implementation;439
62.6;5 UPDATING MESSAGES;441
62.7;6 CONCLUSIONS;443
62.8;Acknowledgements;443
62.9;References;443
63;Chapter 58. A Non-Numeric Approach to Multi-Criteria/Multi-Expert Aggregation Based on Approximate Reasoning;444
63.1;Abstract;444
63.2;1. Introduction;444
63.3;2. PROBLEM FORMULATION;445
63.4;3. A Non-Numeric Technique Multi-Criteria Aggregation;445
63.5;4. Combining Expert's Opinions;446
63.6;5. CONCLUSION;448
63.7;6. REFERENCES;448
64;Chapter 59. Why Do We Need Foundations for Modelling Uncertainties?;449
64.1;1 What Are Foundations?;449
64.2;2 Do We Need Foundations At All?;449
64.3;3 Testability;450
64.4;4 Proliferation and Communication;450
64.5;5 Considering Foundations;450
64.6;6 What Are We Trying to Do?;451
64.7;7 What Are We Talking About?;451
64.8;8 Little but the Truth;451
64.9;9 More of the Truth;452
64.10;10 Usefulness;452
64.11;11 Practise and Theory;452
64.12;12 A Garden;452
64.13;Acknowledgments;453
64.14;References;453
65;Author Index;454