Learning Preference Models with Sparse Interactions of Criteria - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Learning Preference Models with Sparse Interactions of Criteria

Margot Herin
  • Fonction : Auteur
  • PersonId : 1165630
Patrice Perny
Nataliya Sokolovska
  • Fonction : Auteur
  • PersonId : 972554

Résumé

Multicriteria decision making requires defining the result of conflicting and possibly interacting criteria. Allowing criteria interactions in a decision model increases the complexity of the preference learning task due to the combinatorial nature of the possible interactions. In this paper, we propose an approach to learn a decision model in which the interaction pattern is revealed from preference data and kept as simple as possible. We consider weighted aggregation functions like multilinear utilities or Choquet integrals, admitting representations including non-linear terms measuring the joint benefit or penalty attached to some combinations of criteria. The weighting coefficients known as Möbius masses model positive or negative synergies among criteria. We propose an approach to learn the Möbius masses, based on iterative reweighted least square for sparse recovery, and dualization to improve scalability. This approach is applied to learn sparse representations of the multilinear utility model and conjunctive/disjunctive forms of the discrete Choquet integral from preferences examples, in aggregation problems possibly involving more than 20 criteria.
Fichier non déposé

Dates et versions

hal-04103355 , version 1 (23-05-2023)

Identifiants

  • HAL Id : hal-04103355 , version 1

Citer

Margot Herin, Patrice Perny, Nataliya Sokolovska. Learning Preference Models with Sparse Interactions of Criteria. IJCAI 2023 - The 32nd International Joint Conference On Artificial Intelligence, Aug 2023, Macao, China. ⟨hal-04103355⟩
76 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More