eacute; goire

IJCAI Conference 2016 Conference Paper

On Consensus Extraction

• Eacute; ric Gr
• eacute; goire
• S
• eacute; bastien 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.

IJCAI Conference 2007 Conference Paper

• Eacute; ric Gr
• eacute; goire
• Bertrand Mazure
• C
• eacute; dric Piette

In this paper, a new complete technique to compute Maximal Satisfiable Subsets (MSS) and Minimally Unsatisfiable Subformulas (MUS) of sets of Boolean clauses is introduced. The approach improves the currently most efficient complete technique in several ways. It makes use of the powerful concept of critical clause and of a computationally inexpensive local search oracle to boost an exhaustive algorithm proposed by Liffiton and Sakallah. These features can allow exponential efficiency gains to be obtained. Accordingly, experimental studies show that this new approach outperforms the best current existing exhaustive ones.