---
title: "On Consensus Extraction"
authors:
  - "Éric Grégoire"
  - "Sébastien Konieczny"
  - "Jean-Marie Lagniez"
doi: ""
url: "https://ijcai.org/proceedings/2016/159"
pdf: "https://ijcai.org/proceedings/2016/0159.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "1095-1100"
track: ""
keywords:
---

# On Consensus Extraction

Éric Grégoire, Sébastien Konieczny, Jean-Marie Lagniez

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 1095-1100.

## 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 (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.