Abductive inference models for diagnostic problem-solving
Abductive inference models for diagnostic problem-solving
Epistemic entrenchment and possibilistic logic
Artificial Intelligence
A survey of belief revision and updating rules in various uncertainty models
Revision and updating in knowledge bases
Possibilistic causality consistency problem based on asymmetrically-valued causal model
Fuzzy Sets and Systems - Possibility theory and fuzzy logic
Unfair coins and necessity measures: Towards a possibilistic interpretation of histograms
Fuzzy Sets and Systems
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Possibilistic causal models have been proposed as an approach for prediction and diagnosis based on uncertain causal relations. However, the only way to develop the causal models is to acquire the possibilistic knowledge from the experts. The paper proposes an approach to develop the models from a dataset including causes and effects. It first develops a probabilistic causal model, then transform it into a possibilistic one. The points which should be discussed in the approach are 1) the way to transform multiple probabilistic distributions consistently into possibilistic ones, and 2) the merits of the transformation from a probabiistic model into a possibilistic one.