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. Different classes of merging operators are considered including conjunctive, disjunctive, reinforcement and adaptive operators. 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. (shrink)

Preference queries from databases aim to retrieve the best answers w.r.t. user's requirements. The integration of preferences in database queries has known many advances in the last decade. Most of works however are based on comparative preference statements obeying more or less strong semantics. Representing and reasoning about comparative preference statements has also been widely investigated in Artificial Intelligence. In this paper, we bridge the two frameworks and develop a simple and unified framework to reason about preferences in database queries. (...) Our framework is based on insights from non-monotonic reasoning. (shrink)