Ranking Constraints
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 705-710.
Abstract
We need to reason about rankings of objects in a wide variety of domains including information retrieval, sports tournaments, bibliometrics, and statistics. We propose a global constraint therefore for modeling rankings. One important application for rankings is in reasoning about the correlation or uncorrelation between two sequences. For example, we might wish to have consecutive delivery schedules correlated to make it easier for clients and employees, or uncorrelated to avoid predictability and complacence. We therefore also consider global correlation constraints between rankings. For both ranking and correlation constraints, we propose efficient filtering algorithms and decompositions, and report experimental results demonstrating the promise of our proposed approach.
@inproceedings{ijcai2016p106,
title = {Ranking Constraints},
author = {Bessiere, Christian and Hebrard, Emmanuel and Katsirelos, George and Kiziltan, Zeynep and Walsh, Toby},
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 = {705--710},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/106},
}
TY - CPAPER TI - Ranking Constraints AU - Bessiere, Christian AU - Hebrard, Emmanuel AU - Katsirelos, George AU - Kiziltan, Zeynep AU - Walsh, Toby 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 - 705 EP - 710 UR - https://ijcai.org/proceedings/2016/106 ER -Copied
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