Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Decomposable negation normal form
Journal of the ACM (JACM)
A survey on knowledge compilation
AI Communications
Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks
Possibilistic causal networks for handling interventions: a new propagation algorithm
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
On valued negation normal form formulas
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
On the compilation of stratified belief bases under linear and possibilistic logic policies
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Qualitative causal possibilistic networks are important tools for handling uncertain information in the possibility theory framework. Despite their importance, no compilation has been performed to ensure causal reasoning in possibility theory framework. This paper proposes two compilation-based inference algorithms for min-based possibilistic causal networks. The first is a possibilistic adaptation of the probabilistic inference method [8] and the second is a purely possibilistic approach. Both of them are based on an encoding of the network into a propositional theory and a compilation of this output in order to efficiently compute the effect of both observations and interventions, while adopting a mutilation strategy.