Handbook of logic in artificial intelligence and logic programming (vol. 3)
Independence concepts in possibility theory: part I
Fuzzy Sets and Systems
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Background and Perspectives of Possibilistic Graphical Models
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
A Two-Steps Algorithm for Min-Based Possibilistic Causal Networks
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
A Tractable Inference Algorithm for Diagnosing Multiple Diseases
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Possibilistic logic bases and possibilistic graphs
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Bipolar possibilistic representations
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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This paper proposes a new anytime possibilistic inference algorithm for min-based directed networks. Our algorithm departs from a direct adaptation of probabilistic propagation algorithms since it avoids the transformation of the initial networkin to a junction tree which is known to be a hard problem. The proposed algorithm is composed of several, local stabilization, procedures. Stabilization procedures aim to guarantee that local distributions defined on each node are coherent with respect to the ones of its parents. We provide experimental results which, for instance, compare our algorithm with the ones based on a direct adaptation of probabilistic propagation algorithms.