Screen LibraryUncertainty in artificial intelligence : proceedings of the Ninth Conference (1993) : July 9-11, 1993, the Catholic University of America, Washington, D.C. /
Uncertainty in Artificial Intelligence.
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| Corporate Authors: | ; |
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| Group Author: | ; |
| Published: |
Morgan Kaufmann Publishers,
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| Publisher Address: | San Mateo, Calif. : |
| Publication Dates: |
1993. ©1993 |
| Literature type: | eBook |
| Language: | English |
| Subjects: | |
| Online Access: |
http://www.sciencedirect.com/science/book/9781483214511 |
| Summary: |
Uncertainty in Artificial Intelligence. |
| Carrier Form: | 1 online resource (vi, 542 pages) : illustrations |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
9781483214511 1483214516 |
| Index Number: | Q334 |
| CLC: | TP18-532 |
| Contents: |
Front Cover; Uncertainty inArtificialIntelligence; Copyright Page; Table of Contents; Preface; Acknowledgements; Part 1: Foundations; Chapter 1. Causality in Bayesian Belief Networks; Abstract; 1 INTRODUCTION; 2 SIMULTANEOUS EQUATIONS MODELS; 3 CAUSALITY IN BAYESIAN BELIEF NETWORKS; 4 CONCLUSION; Acknowledgments; References; Chapter 2. From Conditional Oughts to Qualitative Decision Theory; Abstract; 1 INTRODUCTION; 2 INFINITESIMALPROBABILITIES, RANKINGFUNCTIONS, CAUSALNETWORKS, AND ACTIONS; 3 SUMMARY OF RESULTS; 4 FROM UTILITIES AND BELIEFS TO GOALS AND ACTIONS. 5 COMBINING ACTIONS AND OBSERVATIONS6 RELATIONS TO OTHER ACCOUNTS; 7 CONCLUSION; Acknowledgements; References; Part 2: Applications and Empirical Comparisons; Chapter 3. A Probabilistic Algorithm for Calculating Structure:Borrowing from Simulated Annealing; Abstract; 1 MOLECULAR STRUCTURE; 2 THE DATA REPRESENTATION; 3 EXPERIMENTS PERFORMED; 4 RESULTS; 5 DISCUSSION; 6 CONCLUSIONS; Acknowledgements; References; Chapter 4. A Study of Scaling Issues in Bayesian Belief Networks for Ship Classification; Abstract; 1 Introduction; 2 Overview; 3 Network Structure; 4 Integration of Belief Values. 5 Discussion6 Conclusion; References; CHAPTER 5. TRADEOFFS IN CONSTRUCTING AND EVALUATING TEMPORAL INFLUENCE DIAGRAMS; Abstract; 1 INTRODUCTION; 2 TEMPORAL BAYESIAN NETWORKS; 3 TID CONSTRUCTION FROM KNOWLEDGE BASES; 4 DOMAIN-SPECIFIC TIME-SERIES MODELS; 5 MODEL SELECTION APPROACHES; 6 EVALUATING TRADEOFFS; 7 RELATED LITERATURE; 8 CONCLUSIONS; Acknowledgements; References; Chapter 6. End-User Construction of Influence Diagrams for Bayesian Statistics; Abstract; 1 INTRODUCTION; 2 STATISTICAL MODEL; 3 SEMANTIC INTERFACE: THE PATIENT-FLOW DIAGRAM. 4 METADATA-STATE DIAGRAM: THE COHORT-STATE DIAGRAM5 CONSTRUCTION STEPS; 6 IMPLEMENTATION; 7 OTHER WORK; 8 CONCLUSION; Acknowledgments; References; Chapter 7. On Considering Uncertainty and Alternatives in Low-Level Vision; Abstract; 1 INTRODUCTION; 2 REGIONS, SEGMENTS, AND SEGMENTATIONS; 3 SEGMENT-LEVEL UNCERTAINTY; 4 SEGMENTATION-LEVEL UNCERTAINTY; 5 REGION-LEVEL UNCERTAINTY; 6 OBTAINING PRIORS; 7 ALGORITHMS; 8 AN EXPERIMENTAL EXAMPLE; 9 CONCLUSION; Acknowledgement; References; Chapter 8. Forecasting Sleep Apnea with Dynamic Network Models; Abstract; 1 INTRODUCTION; 2 RELATED WORK. 3 THE DYNAMIC NETWORK MODEL4 THE DYNEMO IMPLEMENTATION; 5 THE SLEEP-APNEA FORECASTING PROBLEM; 6 CONCLUSIONS; Acknowledgments; References; Chapter 9. Normative Engineering Risk Management Systems; Abstract; 1 INTRODUCTION; 2 ENGINEERING RISK MANAGEMENT SYSTEMS; 3 ADVANCED RISK MANAGEMENT SYSTEM PROJECT; 4 NORMATIVE SYSTEM OVERVIEW; 5 NORMATIVE SYSTEM ACTIVITIES; 6 RESEARCH ISSUES; 7 CONCLUSIONS; Acknowledgements; References; Chapter 10. Diagnosis of Multiple Faults: A Sensitivity Analysis; Abstract; 1 INTRODUCTION; 2 THE MODELS; 3 EXPERIMENTAL DESIGN; 4 RESULTS AND DISCUSSION. |