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On Consensus Extraction

2019-11-22 | |  88 |   47 |   0
Abstract 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 (in propositional 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.

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