Salem Benferhat, Didier Dubois, Souhila Kaci, Henri Prade
The problem of merging multiple sources information is central in many information processing areas such as databases integrating problems, multiple criteria decision making, expert opinion pooling, etc. Recently, several approaches have been proposed to merge classical propositional bases, or sets of (non-prioritized) goals. These approaches are in general semantically defined. Like in belief revision, they use priorities, generally based on Dalal's distance, for merging the classical bases and return a new classical base as a result. An immediate consequence of the generation of a classical base is the impossibility of iterating the fusion process in a coherent way w.r.t. priorities since the underlying ordering is lost. This paper presents a general approach for fusing prioritized bases, both semantically and syntactically, when priorities are represented in the possibilistic logic framework. We show that the approaches which have been recently proposed for merging classical propositional bases can be embedded in this setting. The result is then a prioritized base, and hence the process can be coherently iterated. Moreover, we also provide a syntactic counterpart for the fusion of classical bases. Lastly, the paper briefly discusses rationality postulates for fusing prioritized information.
Keywords: Uncertainty in AI, Belief revision, fusion
Citation: Salem Benferhat, Didier Dubois, Souhila Kaci, Henri Prade: Encoding Information Fusion in Possibilistic Logic: A General Framework for Rational Syntactic Merging. In W.Horn (ed.): ECAI2000, Proceedings of the 14th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2000, pp.3-7.