Learning conditionally lexicographic preference relations
Richard Booth, Yann Chevaleyre, Lang Jérôme, Mengin Jérôme, Chattrakul Sombattheera · Frontiers in artificial intelligence and applications · 2010
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.