Qualitative possibilistic independence based on plausibility relations

Authors:
Nahla Ben Amor;Salem Benferhat;Khaled Mellouli
Affiliations:
Institut Supérieur de Gestion de Tunis;Institut de Recherche en Informatique de Toulouse (I.R.I.T);Institut Supérieur de Gestion de Tunis
Venue:
Technologies for constructing intelligent systems
Year:
2002

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Abstract

Independence is very important in many fields of Artificial Intelligence. It is particularly well studied in Bayesian networks. This paper analysis the notion of independence in the possibility theory framework. More precisely, we propose new definitions of possibilistic independence that we call qualitative independence, which only exploits the plausibility relation induced by a possibility distribution. A comparative study between existing (quantitative) possibilistic independence with qualitative independence is given. Lastly, results on graphoid properties of qualitative independence relations are provided.