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Communication Dans Un Congrès Année : 2019

Preference Elicitation with Uncertainty: Extending Regret Based Methods with Belief Functions

Résumé

Preference elicitation is a key element of any multi-criteria decision analysis (MCDA) problem, and more generally of individual user preference learning. Existing efficient elicitation procedures in the literature mostly use either robust or Bayesian approaches. In this paper, we are interested in extending the former ones by allowing the user to express uncertainty in addition of her preferential information and by modelling it through belief functions. We show that doing this, we preserve the strong guarantees of robust approaches, while overcoming some of their drawbacks. In particular, our approach allows the user to contradict herself, therefore allowing us to detect inconsistencies or ill-chosen model, something that is impossible with more classical robust methods.
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Dates et versions

hal-02417261 , version 1 (18-12-2019)

Identifiants

Citer

Pierre-Louis Guillot, Sébastien Destercke. Preference Elicitation with Uncertainty: Extending Regret Based Methods with Belief Functions. 13th International Conference on Scalable Uncertainty Management (SUM 2019), Dec 2019, Compiègne, France. pp.289-309, ⟨10.1007/978-3-030-35514-2_22⟩. ⟨hal-02417261⟩
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