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

  • Pierre-Louis Guillot
  • Sebastien DesterckeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11940)

Abstract

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.

Keywords

Belief functions Preference elicitation Multicriteria decision 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Heudiasyc laboratoryCompiègneFrance

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