The Complexity of Learning Separable ceteris paribus Preferences
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 848-852.
Abstract
We address the problem of learning preference relations on multi-attribute (or combinatorial) domains. We do so by making a very simple hypothesis about the dependence structure between attributes that the preference relation enjoys, namely separability (no preferential dependencies between attributes). Given a set of examples consisting of comparisons between alternatives, we want to output a separable CP-net, consisting of local preferences on each of the attributes, that fits the examples. We consider three forms of compatibility between a CP-net and a set of examples, and for each of them we give useful characterizations as well as complexity results.Jérôme Lang, Jérôme Mengin
@inproceedings{ijcai2009p145,
title = {The Complexity of Learning Separable ceteris paribus Preferences},
author = {Lang, Jérôme and Mengin, Jérôme},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {848--852},
year = {2009},
url = {https://ijcai.org/proceedings/2009/145},
}
TY - CPAPER TI - The Complexity of Learning Separable ceteris paribus Preferences AU - Lang, Jérôme AU - Mengin, Jérôme T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 848 EP - 852 UR - https://ijcai.org/proceedings/2009/145 ER -Copied
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