---
title: "The Complexity of Learning Separable ceteris paribus Preferences"
authors:
  - "Jérôme Lang"
  - "Jérôme Mengin"
doi: ""
url: "https://ijcai.org/proceedings/2009/145"
pdf: "https://ijcai.org/proceedings/2009/0145.pdf"
venue: "Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence"
year: 2009
pages: "848-852"
track: ""
keywords:
---

# The Complexity of Learning Separable ceteris paribus Preferences

Jérôme Lang, Jérôme Mengin

*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