Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Decomposable negation normal form
Journal of the ACM (JACM)
A survey on knowledge compilation
AI Communications
Journal of Artificial Intelligence Research
Compiling Bayesian networks with local structure
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Experimental comparative study of compilation-based inference in bayesian and possibilitic networks
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
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Min-based possibilistic networks, which are compact representations of possibility distributions, are powerful tools for representing and reasoning with uncertain and incomplete information in the possibility theory framework. Inference in these graphical models has been recently the focus of several researches, especially under compilation. It consists in encoding the network into a Conjunctive Normal Form (CNF) base and compiling this latter to efficiently compute the impact of an evidence on variables. The encoding strategy of such networks can be either locally using local structure or globally using possibilistic local structure. This paper emphasizes on a comparative study between these strategies for compilation-based inference approaches in terms of CNF parameters, compiled bases parameters and inference time.