On Consensus Extraction
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.
@inproceedings{ijcai2016p159,
title = {On Consensus Extraction},
author = {Grégoire, Éric and Konieczny, Sébastien and Lagniez, Jean-Marie},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {1095--1100},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/159},
}
TY - CPAPER TI - On Consensus Extraction AU - Grégoire, Éric AU - Konieczny, Sébastien AU - Lagniez, Jean-Marie T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 1095 EP - 1100 UR - https://ijcai.org/proceedings/2016/159 ER -Copied
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