Uncertainty in Artificial Intelligence

Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, University of Washington, Seattle, July 29-31, 1994

MKP

• Publisher: Morgan Kaufmann
• Year: 2014
• Print ISBN: 9781558603325
• eBook ISBN: 9781483298603
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Uncertainty Proceedings 1994

Table of contents

• Front Cover
• Uncertainty in Artificial Intelligence
• Copyright Page
• Table of Contents
• Preface
• Acknowledgments
• Chapter 1. Ending-based Strategies for Part-of-speech Tagging
• Chapter 2. An evaluation of an algorithm for inductive learning of Bayesian belief networks using simulated data sets
• Chapter 3. Probabilistic Constraint Satisfaction with Non-Gaussian Noise
• Chapter 4. A Bayesian Method Reexamined
• Chapter 5. Laplace's Method Approximations for Probabilistic Inference in Belief Networks with Continuous Variables
• Chapter 6. Generating New Beliefs From Old
• Chapter 7. Counterfactual Probabilities: Computational Methods, Bounds and Applications
• Chapter 8. Modus Ponens Generating Function in the Class of Λ-valuations of Plausibility
• Chapter 9. Approximation Algorithms for the Loop Cutset Problem
• Chapter 10. Possibility and necessity functions over non-classical logics
• Chapter 11. Exploratory Model Building
• Chapter 12. Learning in Multi-Level Stochastic Games with Delayed Information
• Chapter 13. Planning with External Events
• Chapter 14. Properties of Bayesian Belief Network Learning Algorithms
• Chapter 15. A Stratified Simulation Scheme for Inference in Bayesian Belief Networks
• Chapter 16. Proposal: Interactive Media for Research in Uncertainty
• Chapter 17. Efficient Estimation of the Value of Information in Monte Carlo Models
• Chapter 18. Symbolic Probabilistic Inference in large BN20 networks
• Chapter 19. Action Networks: A Framework for Reasoning about Actions and Change under Uncertainty
• Chapter 20. On the Relation between Kappa Calculus and Probabilistic Reasoning
• Chapter 21. A Structured, Probabilistic Representation of Action
• Chapter 22. Integrating Planning and Execution in Stochastic Domains
• Chapter 23. Localized Partial Evaluation of Belief Networks
• Chapter 24. A Probabilistic Model of Action for Least-Commitment Planning with Information Gathering
• Chapter 25. Some Properties of Joint Probability Distributions
• Chapter 26. An ordinal view of independence with application to plausible reasoning
• Chapter 27. Penalty logic and its link with Dempster-Shafer theory
• Chapter 28. Value of Evidence on Influence Diagrams
• Chapter 29. Conditional independence in possibility theory
• Chapter 30. Backward Simulation in Bayesian Networks
• Chapter 31. Learning Gaussian Networks
• Chapter 32. On testing whether an Embedded Bayesian Network represents a probability model
• Chapter 33. Epsilon-Safe Planning
• Chapter 34. Generating Bayesian Networks from Probability Logic Knowledge Bases
• Chapter 35. Abstracting Probabilistic Actions
• Chapter 36. On Modal Logics for Qualitative Possibility in a Fuzzy Setting
• Chapter 37. A New Look at Causal Independence
• Chapter 38. Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
• Chapter 39. A Decision-Based View of Causality
• Chapter 40. Probabilistic Description Logics
• Chapter 41. An Experimental Comparison of Numerical and Qualitative Probabilistic Reasoning
• Chapter 42. An Alternative Proof Method for Possibilistic Logic and its Application to Terminological Logics
• Chapter 43. Possibilistic Conditioning and Propagation
• Chapter 44. The Automated Mapping of Plans for Plan Recognition
• Chapter 45. A Logic for Default Reasoning About Probabilities
• Chapter 46. Optimal Junction Trees
• Chapter 47. From Influence Diagrams to Junction Trees
• Chapter 48. Reduction of Computational Complexity in Bayesian Networks through Removal of Weak Dependences
• Chapter 49. Using New Data to Refine a Bayesian Network
• Chapter 50. Syntax-based default reasoning as probabilistic model-based diagnosis
• Chapter 51. Induction of Selective Bayesian Classifiers
• Chapter 52. Fuzzy Geometric Relations to Represent Hierarchical Spatial Information
• Chapter 53. Constructing Belief Networks to Evaluate Plans
• Chapter 54. Operator Selection While Planning Under Uncertainty
• Chapter 55. Model-Based Diagnosis with Qualitative Temporal Uncertainty
• Chapter 56. Incremental Dynamic Construction of Layered Polytree Networks
• Chapter 57. Models of Consensus for Multiple Agent Systems
• Chapter 58. A Probabilistic Calculus of Actions
• Chapter 59. Robust Planning in Uncertain Environments
• Chapter 60. Anytime Decision Making with Imprecise Probabilities
• Chapter 61. Three Approaches to Probability Model Selection
• Chapter 62. Knowledge Engineering for Large Belief Networks
• Chapter 63. Solving Asymmetric Decision Problems with Influence Diagrams
• Chapter 64. Belief Maintenance in Bayesian Networks
• Chapter 65. Belief Updating by Enumerating High-Probability Independence-Based Assignments
• Chapter 66. Global Conditioning for Probabilistic Inference in Belief Networks
• Chapter 67. Belief Induced by the Partial Knowledge of the Probabilities
• Chapter 68. Ignorance and the Expressiveness of Single- and Set-Valued Probability Models of Belief
• Chapter 69. A probabilistic approach to hierarchical model-based diagnosis
• Chapter 70. Semigraphoids are Two-Antecedental Approximations of Stochastic Conditional Independence Models
• Chapter 71. Exceptional Subclasses in Qualitative Probability
• Chapter 72. A Defect in Dempster-Shafer Theory
• Chapter 73. State-Space Abstraction for Anytime Evaluation of Probabilistic Networks
• Chapter 74. General Belief Measures
• Chapter 75. Generating Graphoids from Generalised Conditional Probability
• Chapter 76. On Axiomatization of Probabilistic Conditional Independencies
• Chapter 77. Evidential Reasoning with Conditional Belief Functions
• Chapter 78. Intercausal Independence and Heterogeneous Factorization
• Author Index