Bibliografie

(eng): The article addresses a lazy learning approach to fully probabilistic decision making when a decision maker (human or arti_cial) uses incomplete knowledge of environment and faces high computational limitations. The resulting lazy Fully Probabilistic Design (FPD) selects a decision strategy that moves a probabilistic description of the closed decision loop to a pre-speci_ed ideal description. The lazy FPD uses currently observed data to _nd past closed-loop similar to the actual ideal model. The optimal decision rule of the closest model is then used in the current step. The e_ectiveness and capability of the proposed approach are manifested through example.