Uncertainty in artificial intelligence 5
by Henrion, Max
This volume, like its predecessors, reflects the cutting edge of research on the automation of reasoning under uncertainty. A more pragmatic emphasis is evident, for although some papers address fundamental issues, the majority address practical issues. Topics include the relations between alternative formalisms (including possibilistic reasoning), Dempster-Shafer belief functions, non-monotonic reasoning, Bayesian and decision theoretic schemes, and new inference techniques for belief nets. Ne…
Read More ↓Title Statements
- Full title :Uncertainty in artificial intelligence 5 / edited by Max Henrion [and others].
Summary
This volume, like its predecessors, reflects the cutting edge of research on the automation of reasoning under uncertainty. A more pragmatic emphasis is evident, for although some papers address fundamental issues, the majority address practical issues. Topics include the relations between alternative formalisms (including possibilistic reasoning), Dempster-Shafer belief functions, non-monotonic reasoning, Bayesian and decision theoretic schemes, and new inference techniques for belief nets. New techniques are applied to important problems in medicine, vision, robotics, and natural language und.
Read More ↓Edition Statements
- Publication Date:©1990
Publication Statements
- Publisher(s):North-Holland
- Place of Publication:Amsterdam
- Language:eng
- Publication Date:©1990
The Physical Object
- Description:1 online resource (xiv, 459 pages) : : illustrations
Edition Identifiers
- ISBN:9781483296555, 1483296555
- oclc:ocn893436290
- LC Classification:Q335
- Dewey Decimal:006.3
Table of Contents
Front Cover; Uncertainty in Artificial Intelligence 5; Copyright Page; Preface; Table of Contents; Reviewers; Program Committee; Contributors; PART I: FUNDAMENTAL ISSUES; Chapter 1. Lp-A Logic for Statistical Information; 1 Introduction; 2 Other Probability Logics; 3 Types of Statistical Knowledge; 4 Syntax and Semantics; 5 Syntax; 6 Examples of Representation; 7 Deductive Proof Theory; 8 Degrees of Belief; Acknowledgments; References; CHAPTER2. REPRESENTING TIME IN CAUSAL PROBABILISTIC NETWORKS; 1 INTRODUCTION1; 2 THE DISTRIBUTION OF TIME; 3 MARKED POINT PROCESS REPRESENTATION.
4 NETWORKS OF ""DATES""Acknowledgements; References; CHAPTER3. CONSTRUCTING THE PIGNISTIC PROBABILITY FUNCTION IN A CONTEXT OF UNCERTAINTY; 1. Introduction; 2. The credibility function; 3. a-combined credibility spaces; 4. The pignistic probability function; 5. Co-credibility function; 6. The Moebius transformations of Cr; 7. Conclusions; Bibliography; Acknowledgements; Chapter 4. Can Uncertainty Management Be Realized In A Finite Totally Ordered Probability Algebra?; 1 Introduction; 2 Finite totally ordered probability algebras; 3 Bayes theorem and reasoning by case.
4 Problems with legal finite totally…
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