Department of Pattern Recognition

(eng): Bayesian decision theory provides a strong theoretical basis for a singleparticipant decision making under uncertainty, that can be extended to multipleparticipant decision making. However, this theory (similarly as others) assumes unlimited abilities of a participant to probabilistically model the participant’s environment and to optimise its decision-making strategy. The proposed methodology solves knowledge and preference elicitation, as well as sharing of individual, possibly fragmental, knowledge and preferences among imperfect participants. The approach helps to overcome the non-realistic assumption on participants’ unlimited abilities.