Uncertainty in Artificial Intelligence: Proceedings of the Eighth Conference (1992) covers the papers presented at the Eighth Conference on Uncertainty in Artificial Intelligence, held at Stanford University on July 17-19, 1992. The book focuses on the processes, methodologies, technologies, and approaches involved in artificial intelligence. The selection first offers information on Relative Evidential Support (RES), modal logics for qualitative possibility and beliefs, and optimizing causal orderings for generating DAGs from data. Discussions focus on reversal, swap, and unclique operators, modal representation of possibility, and beliefs and conditionals. The text then examines structural controllability and observability in influence diagrams, lattice-based graded logic, and dynamic network models for forecasting. The manuscript takes a look at reformulating inference problems through selective conditioning, entropy and belief networks, parallelizing probabilistic inference, and a symbolic approach to reasoning with linguistic quantifiers. The text also ponders on sidestepping the triangulation problem in Bayesian net computations; exploring localization in Bayesian networks for large expert systems; and expressing relational and temporal knowledge in visual probabilistic networks. The selection is a valuable reference for researchers interested in artificial intelligence.

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Yes, you can access Uncertainty in Artificial Intelligence by Didier J. Dubois,Michael P. Wellman,Bruce D'Ambrosio in PDF and/or ePUB format, as well as other popular books in Informatique & Intelligence artificielle (IA) et sémantique. We have over one million books available in our catalogue for you to explore.

Information

Publisher

Morgan Kaufmann

Year

2014

ISBN

9781483282879

Topic

Informatique

Subtopic

Intelligence artificielle (IA) et sémantique

Front Cover
Uncertainty in Artificial Intelligence
Copyright Page
Table of Contents
Preface
Acknowledgments
Chapter 1. RεS—A Relative Method for Evidential Reasoning
Chapter 2. Optimizing Causal Orderings for Generating DAGs from Data
Chapter 3. Modal Logics for Qualitative Possibility and Beliefs
Chapter 4. Structural Controllability and Observability in Influence Diagrams
Chapter 5. Lattice-Based Graded Logic: A Multimodal Approach
Chapter 6. Dynamic Network Models for Forecasting
Chapter 7. Reformulating Inference Problems Through Selective Conditioning
Chapter 8. Entropy and Belief Networks
Chapter 9. Parallelizing Probabilistic Inference Some Early Explorations
Chapter 10. Objection-Based Causal Networks
Chapter 11. A Symbolic Approach to Reasoning with Linguistic Quantifiers
Chapter 12. Possibilistic Assumption based Truth Maintenance System, Validation in a Data Fusion Application
Chapter 13. An entropy-based learning algorithm of Bayesian conditional trees
Chapter 14. Knowledge integration for conditional probability assessments
Chapter 15. Integrating Model Construction and Evaluation
Chapter 16. Reasoning With Qualitative Probabilities Can Be Tractable
Chapter 17. A computational scheme for reasoning in dynamic probabilistic networks
Chapter 18. The Dynamic of Belief in the transferable belief model and Specialization-Generalization Matrices
Chapter 19. A NOTE ON THE MEASURE OF DISCORD
Chapter 20. Semantics for Probabilistic Inference
Chapter 21. Some Problems for Convex Bayesians
Chapter 22. Bayesian Meta-Reasoning: Determining Model Adequacy from Within a Small World
Chapter 23. The Bounded Bayesian
Chapter 24. Representing Context-Sensitive Knowledge in a Network Formalism: A Preliminary Report
Chapter 25. A Probabilistic Network of Predicates
Chapter 26. Representing Heuristic Knowledge in D-S Theory
Chapter 27. The Topological Fusion of Bayes Nets
Chapter 28. Calculating Uncertainty Intervals From Conditional Convex Sets of Probabilities
Chapter 29. Sensor Validation using Dynamic Belief Networks
Chapter 30. Empirical Probabilities in Monadic Deductive Databases
Chapter 31. aHUGIN: A System Creating Adaptive Causal Probabilistic Networks
Chapter 32. MESA: Maximum Entropy by Simulated Annealing
Chapter 33. Decision Methods for Adaptive Task-Sharing in Associate Systems
Chapter 34. Modeling Uncertain Temporal Evolutions in Model-Based Diagnosis
Chapter 35. Guess-And-Verify Heuristics for Reducing Uncertainties in Expert Classification Systems
Chapter 36. R&D Analyst: An Interactive Approach to Normative Decision System Model Construction
Chapter 37. Possibilistic Constraint Satisfaction Problems or "How to handle soft constraints ?"
Chapter 38. Decision Making Using Probabilistic Inference Methods
Chapter 39. Conditional Independence in Uncertainty Theories
Chapter 40. The Nature of the unnormalized Beliefs encountered in the Transferable Belief Model
Chapter 41. Intuitions about Ordered Beliefs Leading to Probabilistic Models
Chapter 42. Expressing Relational and Temporal Knowledge in Visual Probabilistic Networks
Chapter 43. A Fuzzy Logic Approach to Target Tracking
Chapter 44. Towards Precision of Probabilistic Bounds Propagation
Chapter 45. An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation
Chapter 46. Generalizing Jeffrey Conditionalization
Chapter 47. INTERVAL STRUCTURE: A Framework for Representing Uncertain Information
Chapter 48. Exploring Localization In Bayesian Networks For Large Expert Systems
Chapter 49. A Decision Calculus for Belief Functions in Valuation-Based Systems
Chapter 50. Sidestepping the Triangulation Problem in Bayesian Net Computations
Author Index