Communication Dans Un Congrès Année : 2024

Noise-Tolerant Active Preference Learning for Multicriteria Choice Problems

Résumé

To make a choice in the presence of multiple criteria, we generally use an aggregation function which determines, for each alternative, the balance of its strengths and weaknesses and its overall evaluation. The aggregation function uses weights to adapt the model to the decision-maker's value system, by specifying the importance of the criteria and possibly their interactions. In this paper, we propose a noise-tolerant active learning method for these parameters, which not only effectively reduces the indeterminacy of the weights to identify an optimal or near-optimal decision among a given set of alternatives, but also simultaneously determines a predictive model of preferences capable of making relevant choices for the decision-maker on new instances. These outcomes are achieved by leveraging a general disagreement-based active learning approach that is theoretically guaranteed to be tolerant to noisy answers. The proposed method applies to various weighted aggregation functions, linear or not, classically used in decision theory.

Fichier principal
Vignette du fichier
ADT24_Preprint.pdf (376.26 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

hal-04841741 , version 1 (16-12-2024)

Licence

Identifiants

Citer

Margot Herin, Patrice Perny, Nataliya Sokolovska. Noise-Tolerant Active Preference Learning for Multicriteria Choice Problems. Algorithmic Decision Theory (ADT 2024), Oct 2024, New Brunswick, NJ, United States. pp.191--206, ⟨10.1007/978-3-031-73903-3_13⟩. ⟨hal-04841741⟩
222 Consultations
240 Téléchargements

Altmetric

Partager

  • More