Ing. Jakub Štěch
Date and time: 24.04.2017 - 11:00
Details:The seminar covers a lazy learning approach to fully probabilistic decision making when a decision maker (human or articial) uses incomplete knowledge of environment and faces high computational limitations. The resulting lazy fully probabilistic design selects a decision strategy that makes a probabilistic description of the closed decision loop close to a pre-specied closed-loop ideal description. Instead of classical learning an environment model followed by optimisation, the lazy FPD uses the currently observed data to end the past closed-loop similar to the actual ideal model. The optimal decision rule of the closest model is then used in the current step.