Authors:
Amen Ajroud
1
;
Mohamed Nazih Omri
1
;
Salem Benferhat
2
and
Habib Youssef
3
Affiliations:
1
FSM, Université de Monastir, Tunisia
;
2
CRIL, Université d’Artois, France
;
3
ISITCOM, Université de Sousse, Tunisia
Keyword(s):
Possibilistic networks, possibility distributions, approximate inference, DAG multiply connected.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Bayesian Networks
;
Enterprise Information Systems
;
Soft Computing
Abstract:
Product-Based Possibilistic Networks appear to be important tools to efficiently and compactly represent possibility distributions. The inference process is a crucial task to propagate information into network when new pieces of information, called evidence, are observed. However, this inference process is known to be a hard task especially for multiply connected networks. In this paper, we propose an approximate algorithm for product-based possibilistic networks. More precisely, we propose an adaptation of the probabilistic approach “Loopy Belief Propagation” (LBP) for possibilistic networks.