UID:
edoccha_9960073716402883
Umfang:
1 online resource (522 p.)
ISBN:
1-4832-9652-0
Serie:
Machine Intelligence and Pattern Recognition ; Volume 4
Inhalt:
How to deal with uncertainty is a subject of much controversy in Artificial Intelligence. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy.〈br〉〈br〉Some of the notable issues which emerge from these papers revolve around an interval-based calculus of uncertainty, the Dempster-Shafer Theory, and probability as the best numeric model for uncertainty. There remain strong dissenting opinions not only about probability but even about the utility of any numeric method in this context.〈br〉〈br〉
Anmerkung:
Description based upon print version of record.
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Front Cover; Uncertainty in Artificial Intelligence; Copyright Page; PREFACE; CONTRIBUTORS; Table of Contents; PARTI: OVERVIEWS AND REVIEWS; Chapter 1.Handling Uncertain Information : A Review of Numeric and Non-numeric Methods; 1. Introduction; 2. Representatiori Of UncertainInformation; 3. Probability; 4. Evidence Theory; 5. Possibility Theory; 6. Non-numeric methods; 7. Theory of Endorsements; 8.Combination Of Bodies Of Uncertain Information; 9.Drawing Inferences From Uncertain Information; 10. Conclusions; References; CHAPTER2. CONSENSUS RULES; 1. INTRODUCTION
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2. SOME CONSENSUS RULE CHARACTERISTICS3. CHOICE OF WEIGHTS FOR LINEAR POOLS - UPDATING; 4. CONCLUDING REMARKS; REFERENCES; PARTII: EXPLICATION OR CRITIQUE OF CURRENT APPROACHES TO UNCERTAINTY; Chapter 3.Uncertainty Handling in Expert Systems: Uniform vs. Task-Specific Formalisms; 1. Is There An ""Uncertainty Handling"" Problem?; 2. Conflation of Different Aims; 3. Uncertainty Handling in Classification Problem Solving; 4. Concluding Remarks; References; CHAPTER4. PROBABILISTIC REASONING IN PREDICTIVE EXPERT SYSTEMS; 1. INTRODUCTION; 2. AN EXAMPLE
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3. 'EXCHANGEABILITY' AND DOUBT ABOUT PROBABILITIES4. DISCUSSION: WHEN IS PROBABILITY APPROPRIATE ?; REFERENCES; CHAPTER5. A FRAMEWORK FOR COMPARING UNCERTAIN INFERENCE SYSTEMS TO PROBABILITY; 1. INTRODUCTION; 2. FORMALISMS FOR REPRESENTING UNCERTAINTY; 3. COMPARING UIS'S; 4. EVALUATING DIFFERENCES IN RESULTS; 5. COMPARING PERFORMANCE ON AN EXAMPLE RULE-SET; 6. FINAL REMARKS; References; Chapter 6.Probabilistic versus Fuzzy Reasoning; 1 Introduction; 2 The Probabilistic Approach; 3 Combining Evidence; 4 ""Conflicting Evidence""; 5 Uncertain Evidence; 6 The Fuzzy Approach; 7 Conclusions
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ReferencesChapter 7.Is Probability Theory Sufficient for Dealing with Uncertainty in AI: A Negative View; 1. The Issue of Adequacy; 2. Inference; REFERENCES AND RELATED PUBLICATIONS; CHAPTER8. CONFIDENCE FACTORS, EMPIRICISM AND THE DEMPSTER-SHAFER THEORY OF EVIDENCE; REFERENCES; CHAPTER9. PROBABILITY JUDGMENT IN ARTIFICIAL INTELLIGENCE; 1. Two Probability Languages; 2. Three Examples; 3. Probability Judgment in Expert Systems; References; CHAPTER 10. THE INCONSISTENT USE OF MEASURES OF CERTAINTY IN ARTIFICIAL INTELLIGENCERESEARCH; 1. INTRODUCTION
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2. DISTINGUISHING BELIEF UPDATES FROM ABSOLUTE BELIEFS3. HISTORICAL BLURRING OF BELIEF AND BELIEF UPDATE; 4. INTUITIVE PROPERTIES OF MEASURES OF BELIEF; 5. PROPERTIES OF BELIEF UPDATES; 6. A PROBABILISTIC BELIEF UPDATE; 7. INCONSISTENCY OF EQUATING ABSOLUTE BELIEFS WITH BELIEF UPDATES; 8. EVIDENCE COMBINATION AND MODULARITY; 9. THE MODULAR UPDATING PARADIGM; 10. INCONSISTENT USE OF THE MODULAR UPDATING PARADIGM; 11. MODULAR BELIEF UPDATING IN MYCIN; 12. MODULAR BELIEF UPDATING IN INTERNIST-1; 13. CONSEQUENCES OF THE INCONSISTENCY; 14. RELEVANCE OF BIASES IN THE ELICITATION OF BELIEF
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15. SUMMARY
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English
Weitere Ausg.:
ISBN 1-322-47225-4
Weitere Ausg.:
ISBN 0-444-70058-7
Sprache:
Englisch
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