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)
Independence concepts in possibility theory: part I
Fuzzy Sets and Systems
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Independence and Possibilistic Conditioning
Annals of Mathematics and Artificial Intelligence
Compiling propositional weighted bases
Artificial Intelligence - Special issue on nonmonotonic reasoning
Graphoid properties of qualitative possibilistic independence relations
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Inference with idempotent valuations
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Mastering the Processing of Preferences by Using Symbolic Priorities in Possibilistic Logic
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Fuzzy sets in machine learning and data mining
Applied Soft Computing
Representing belief function knowledge with graphical models
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
Efficient processing of twig query with compound predicates in fuzzy XML
Fuzzy Sets and Systems
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Possibilistic networks and possibilistic logic are two standard frameworks of interest for representing uncertain pieces of knowledge. Possibilistic networks exhibit relationships between variables while possibilistic logic ranks logical formulas according to their level of certainty. 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. It results from combining the two semantically equivalent types of standard representation. We first present a propagation algorithm through hybrid possibilistic networks. This inference algorithm on hybrid networks is strictly more efficient (and confirmed by experimental studies) than the one of standard propagation algorithm.