Artificial Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Decomposable negation normal form
Journal of the ACM (JACM)
A general non-probabilistic theory of inductive reasoning
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
A compiler for deterministic, decomposable negation normal form
Eighteenth national conference on Artificial intelligence
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
Compiling Bayesian networks using variable elimination
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Compiling Bayesian networks with local structure
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Inferring interventions in product-based possibilistic causal networks
Fuzzy Sets and Systems
Compiling min-based possibilistic causal networks: a mutilated-based approach
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
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Qualitative possibilistic causal networks are important tools for handling uncertain information in the possibility theory framework. Contrary to possibilistic networks (Ayachi et al., 2011 [2]), the compilation principle has not been exploited to ensure causal reasoning in the possibility theory framework. This paper proposes mutilated-based inference approaches and augmented-based inference approaches for qualitative possibilistic causal networks using two compilation methods. The first one is a possibilistic adaptation of the probabilistic inference approach (Darwiche, 2002 [13]) and the second is a purely possibilistic approach based on the transformation of the graphical-based representation into a logic-based one (Benferhat et al., 2002 [3]). Each of the proposed methods encodes the network or the possibilistic knowledge base into a propositional base and compiles this output in order to efficiently compute the effect of both observations and interventions. This paper also reports on a set of experimental results showing cases in which augmentation outperforms mutilation under compilation and vice versa.