Using and Learning GAI-Decompositions for Representing Ordinal Rankings

Damien Bigot (1) , Hélène Fargier (1, 2) , Jérôme Mengin (1) , Bruno Zanuttini (3)

1 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage
2 CNRS - Centre National de la Recherche Scientifique
3 Equipe MAD - Laboratoire GREYC - UMR6072

We study the use of GAI-decomposable utility functions for representing ordinal rankings on combinatorial sets of objects. Considering only the relative order of objects leaves a lot of freedom for choosing a particular utility function, which allows one to get more compact representations. We focus on the problem of learning such representations, and give a polynomial PAC-learner for the case when a constant bound is known on the degree of the target representation. We also propose linear programming approaches for minimizing such representations.

Domaines: Intelligence artificielle [cs.AI]

Damien Bigot, Hélène Fargier, Jérôme Mengin, Bruno Zanuttini. Using and Learning GAI-Decompositions for Representing Ordinal Rankings. Workshop on Preference Learning (PL 2012) @ ECAI 2012, Aug 2012, Montpellier, France. pp.5-10. ⟨hal-00946859⟩