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Uncertainty in artificial intelligence 5 /
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 alternati...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam ; New York :
North-Holland,
1990.
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| Colección: | Machine intelligence and pattern recognition ;
v. 10. |
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
Tabla de Contenidos:
- 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 ordered probability5 An experiment; 6 Conclusion; Acknowledgements; References; Appendix A: Derivation of; Appendix B: Examples of legal FTOPAs; Appendix C; PART Il: DEFEASIBLE REASONING AND UNCERTAINTY; Chapter 5. Defeasible Reasoning and Uncertainty: Comments; 1 Overview; 2 Goldszmidt & Pearl; 3 Bonissone et al; 4 Loui; 5 Reference Classes: What They Didn't Talk About, But Somebody Should!; Acknowledgements; References; Chapter 6. Uncertainty and Incompleteness: Breaking the Symmetry of Defeasible Reasoning ; 1 Introduction; 2 Plausible Reasoning Module.
- 3 Finding Admissible Labelings4 Algorithms and Heuristics; 5 Conclusions; References; Chapter 7. Deciding Consistency of Databases Containing Defeasible and Strict Information; 1 Introduction; 2 Notation and Preliminary Definitions; 3 Probabilistic Consistency and Entailment; 4 An Effective Procedure for Testing Consistency; 5 Examples; 6 Conclusions; Acknowledgments; References; CHAPTER8. DEFEASIBLE DECISIONS: WHAT THE PROPOSAL IS AND ISN'T; 1 WHAT THE PROPOSAL IS; 2 WHAT THE PROPOSAL ISN'T; 3 AN OPEN CONVERSATION WITH RAIFFA.
- CHAPTER9. CONDITIONING ON DISJUNCTIVE KNOWLEDGE: SIMPSON'S PARADOX IN DEFAULT LOGIC1. INTRODUCTION; 2. DOES AN EMU OR OSTRICH RUN?; 3. ARTS STUDENTS AND SCIENCE STUDENTS; 4. DISCUSSION OF THE PARADOX; 6. CONCLUSIONS; ACKNOWLEDGEMENTS; REFERENCES; PART Ill: ALGORITHMS FOR INFERENCE IN BELIEF NETS; Chapter 10. An Introduction to Algorithms for Inference in Belief Nets; 1. Introduction; 2. Qualitative, real, and interval-valued belief representations; 3. Early approaches; 4. Exact methods; 5. Two level belief networks; 6. Stochastic simulation and Monte Carlo schemes; 7. Final remarks.