Automated Preference Elicitation for Decision Making

In the contemporary complex world decisions are made by an imperfect participant devoting limited deliberation resources to any decision-making task. A normative decision-making (DM) theory should provide support systems allowing such a participant to make rational decisions in spite of the limited resources. Efficiency of the support systems depends on the interfaces enabling a participant to benefit from the support while exploiting the gradually accumulating knowledge about DM environment and respecting incomplete, possibly changing, participant’s DM preferences. The insufficiently elaborated preference elicitation makes even the best DM supports of a limited use. This chapter proposes a methodology of automatic eliciting of a quantitative DM preference description, discusses the options made and sketches open research problems. The proposed elicitation serves to fully probabilistic design, which includes a standard Bayesian decision making.