Learning conditionally lexicographic preference relations

Abstract. We consider the problem of learning a user's ordinal preferences on a multiattribute domain, assuming that her preferences are lexicographic. We introduce a general graphical representation called LP-trees which captures various natural classes of such preference relations, depending on whether the importance order between attributes and/or the local preferences on the domain of each attribute is conditional on the values of other attributes. For each class we determine the Vapnik-Chernovenkis dimension, the communication complexity of preference elicitation, and the complexity of identifying a model in the class consistent with a set of user-provided examples.

• Type: Journal
• Year: 2010
• Where: ECAI
• Authors: Richard Booth, Yann Chevaleyre, Jérôme Lang, Jérôme Mengin, Chattrakul Sombattheera
• Keywords: Artificial Intelligence, ECAI 2010, General Graphical Representation, Preferences, Various Natural Classes