Uncertainty in artificial intelligence
by Kanal, Laveen N. Lemmer, John F.
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. 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 pr…
Read More ↓Title Statements
- Full title :Uncertainty in artificial intelligence / edited by Laveen N. Kanal and John F. Lemmer.
Summary
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. 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.
Read More ↓Edition Statements
- Publication Date:©1986
Publication Statements
- Publisher(s):North-Holland
- Place of Publication:Amsterdam, Netherlands
- Language:eng
- Publication Date:©1986
The Physical Object
- Description:1 online resource (522 pages).
Edition Identifiers
- ISBN:9781483296524, 1483296520
- oclc:ocn900885832
- LC Classification:Q335
- Dewey Decimal:006.3/1
Table of Contents
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.
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.
3. 'EXCHANGEABILITY' AND DOUBT ABOUT PROBABILITIES4. DISCUSSION: WH…
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