Abstract
The article addresses a lazy learning approach to fully probabilistic decision making when a decision maker (human or artificial) 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-specified ideal description. The lazy FPD uses currently observed data to find 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 effectiveness and capability of the proposed approach are manifested through example.
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Acknowledgement
The authors would like to thank Miroslav Kárný for valuable discussions and comments. The research has been partially supported by the Czech Science Foundation, project GA16-09848S.
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Guy, T.V., Derakhshan, S.F., Štěch, J. (2018). Lazy Fully Probabilistic Design: Application Potential. In: Belardinelli, F., Argente, E. (eds) Multi-Agent Systems and Agreement Technologies. EUMAS AT 2017 2017. Lecture Notes in Computer Science(), vol 10767. Springer, Cham. https://doi.org/10.1007/978-3-030-01713-2_20
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DOI: https://doi.org/10.1007/978-3-030-01713-2_20
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