The complexity of learning separable ceteris paribus preferences


Conference Paper

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.

Duke Authors

Cited Authors

  • Lang, J; Mengin, J

Published Date

  • January 1, 2009

Published In

Start / End Page

  • 848 - 853

International Standard Serial Number (ISSN)

  • 1045-0823

International Standard Book Number 13 (ISBN-13)

  • 9781577354260

Citation Source

  • Scopus