On Consensus Extraction / 1095
éric Grégoire, Sébastien Konieczny, Jean Marie Lagniez
Computing a consensus is a key task in various AI areas, ranging from belief fusion, social choice,negotiation, etc. In this work, we define consensus operators as functions that deliver parts of the set-theoretical union of the information sources (inpropositional logic) to be reconciled, such that no source is logically contradicted. We also investigate different notions of maximality related to these consensuses. From a computational point of view, we propose a generic problem transformation that leads to a method that proves experimentally efficient very often, even for large conflicting sources to be reconciled.