Automated Preferences Elicitation

Systems supporting decision making became almost inevitable in the modern complex world. Their efficiency depends on the sophisticated interfaces enabling a user take advantage of the support while respecting the increasing on-line information and incomplete, dynamically changing user’s preferences. The best decision making support is useless without the proper preference elicitation. The paper proposes a methodology supporting automatic learning of quantitative description of preferences. The proposed elicitation serves to fully probabilistic design, which is an extension of Bayesian decision making.