Élicitation possibiliste de préférences avec un regret Minimax

Identifying the preferences of a user through elicitation is a central part of multi-criteria decision aid (MCDA) or preference learning tasks. Incremental elicitation through a robust approach is a classic method. The robust approach has strong guarantees through very strong hypotheses, but cannot integrate uncertain information. In this paper, we propose and test a method based on possibility theory, which keeps the guarantees of the robust approach without needing its strong hypotheses. Among other things, we show that it can detect user errors as well as model misspecification.