The Complexity of Learning Separable Ceteris Paribus Preferences
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. J�r�me Lang, J�r�me Mengin
Cite
Text
Lang and Mengin. "The Complexity of Learning Separable Ceteris Paribus Preferences." International Joint Conference on Artificial Intelligence, 2009.Markdown
[Lang and Mengin. "The Complexity of Learning Separable Ceteris Paribus Preferences." International Joint Conference on Artificial Intelligence, 2009.](https://mlanthology.org/ijcai/2009/lang2009ijcai-complexity/)BibTeX
@inproceedings{lang2009ijcai-complexity,
title = {{The Complexity of Learning Separable Ceteris Paribus Preferences}},
author = {Lang, Jérôme and Mengin, Jérôme},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2009},
pages = {848-853},
url = {https://mlanthology.org/ijcai/2009/lang2009ijcai-complexity/}
}