Uncertainty in Artificial Intelligence: Proceedings of the ... Conference, Issue 10

Front Cover
Morgan Kaufmann, 1994 - Artificial intelligence

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Contents

Endingbased Strategies for PartofSpeech Tagging
1
An Evaluation of an Algorithm for Inductive Learning
8
Probabilistic Constraint Satisfaction with NonGaussian Noise
15
A Bayesian Method Reexamined
23
Generating New Beliefs from Old
37
Computational Methods Bounds and Applications
46
Modus Ponens Generating Function in the Class of AValuations of Plausibility
55
Possibility and Necessity Functions over NonClassical Logics
69
EpsilonSafe Planning
253
Generating Bayesian Networks from Probablity Logic Knowledge Bases
263
Abstracting Probabilistic Actions
270
On Modal Logics for Qualitative Possibility in a Fuzzy Setting
278
A New Look at Causal Independence
286
The Combination of Knowledge and Statistical Data
293
A Decisionbased View of Causality
302
An Experimental Comparison of Numerical and Qualitative Probabilistic Reasoning
319

Exploratory Model Building
77
Learning in MultiLevel Stochastic Games with Delayed Information
86
Planning with External Events
94
Properties of Bayesian Belief Network Learning Algorithms
102
A Stratified Simulation Scheme for Inference in Bayesian Belief Networks
110
Interactive Media for Research in Uncertainty
118
Symbolic Probabilistic Inference in Large BN20 Networks
128
A Framework for Reasoning about Actions and Change under Uncertainty
136
On the Relation between Kappa Calculus and Probabilistic Reasoning
145
A Structured Probabilistic Representation of Action
154
Localized Partial Evaluation of Belief Networks
170
A Probablistic Model of Action for LeastCommitment Planning with Information Gathering
178
Some Properties of Joint Probability Distributions
187
An Ordinal View of Independence with Application to Plausible Reasoning
195
Penalty Logic and its Link with DempsterShafer Theory
204
Value of Evidence on Influence Diagrams
212
Conditional Independence in Possibility Theory
221
Backward Simulation in Bayesian Networks
227
Learning Gaussian Networks
235
On Testing Whether an Embedded Bayesian Network Represents a Probability Model
244
Possibilistic Conditioning and Propagation
336
A Logic for Default Reasoning About Probabilities
352
From Influence Diagrams to Junction Trees
367
Using New Data to Refine a Bayesian Network
383
Induction of Selective Bayesian Classifiers
399
Constructing Belief Networks to Evaluate Plans
416
Modelbased Diagnosis with Qualitative Temporal Uncertainty
432
Models of Consensus for Multiple Agent Systems
447
Anytime Decision Making with Imprecise Probabilities
470
Knowledge Engineering for Large Belief Networks
484
Belief Maintenance in Bayesian Networks
498
Global Conditioning for Probabilistic Inference in Belief Networks
514
Ignorance and the Expressiveness of Single and SetValued Probability Models of Belief
531
Semigraphoids Are TwoAntecedental Approximations
546
A Defect in DempsterShafer Theory
560
General Belief Measures
575
On Axiomatization of Probabilistic Conditional Independencies
591
Intercausal Independence and Heterogeneous Factorization
606
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