Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Handbook of logic in artificial intelligence and logic programming (vol. 3)
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Compiling propositional weighted bases
Artificial Intelligence - Special issue on nonmonotonic reasoning
IEEE Transactions on Fuzzy Systems
International Journal of Approximate Reasoning
On the Use of Possibilistic Bases for Local Computations in Product-Based Possibilistic Networks
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Inferring interventions in product-based possibilistic causal networks
Fuzzy Sets and Systems
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Possibilistic networks are important tools for dealing with uncertain pieces of information. For multiply-connected networks, it is well known that the inference process is a hard problem. This paper studies a new representation of possibilistic networks, called hybrid possibilistic networks. The uncertainty is no longer represented by local conditional possibility distributions, but by their compact representations which are possibilistic knowledge bases. We show that the inference algorithm in hybrid networks is strictly more efficient than the ones of standard propagation algorithm.