Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Possibilistic causal networks for handling interventions: a new propagation algorithm
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Revision sequences and nested conditionals
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
On the revision of probabilistic beliefs using uncertain evidence
Artificial Intelligence
A Framework for Iterated Belief Revision Using Possibilistic Counterparts to Jeffrey's Rule
Fundamenta Informaticae - Methodologies for Intelligent Systems
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
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This paper deals with two important issues related to the handling of uncertain and causal information in a qualitative (or min-based) possibility theory framework. The first issue addresses encoding interventions using the possibilistic conditioning under uncertain inputs problem. More precisely, we analyze the min-based possibilistic counterpart of Jeffrey's rule of conditioning and point out that contrary to the probabilistic setting, this rule does not guarantee the existence of a solution satisfying the kinematics conditions. Then we show that this rule can naturally encode the concept of interventions in causal graphical models. Surprisingly enough, we show that when dealing with interventions the min-based counterpart of Jeffrey's rule provides a unique solution. The second issue deals with the efficient handling of sets of observations and interventions in min-based possibilistic networks, where we propose a solution based on a series of equivalent and efficient transformations on the initial causal graph.