0483831 20240103215240.220180102235959.9 85055486182 10.1007/978-3-030-01713-2_20 11 s. P cav_un_epca*0483465978-3-030-01712-5281-291ChamSpringer2018BelardinelliF. lazy learning fully probabilistic design decision making linear quadratic Gaussian control cav_un_auth*0101092 Guy Tatiana Valentine UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0355639 Department of Adaptive Systems Fakhimi Derakhshan Siavash UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0355640 Štěch Jakub UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems CZ Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2017/AS/guy-0483831.pdf cav_un_auth*0331019 GA16-09848S GA ČR 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. cav_un_auth*0355398 European Conference on Multi-Agent Systems (EUMAS) 2017 /15./ 20171214 20171215 Évry FR BC 10000 10200 10201 2019 PR RVO:67985556 http://hdl.handle.net/11104/0278988 S 2018 85055486182 SCOPUS PUBMED WOS cav_un_epca*0483465 Multi-Agent Systems and Agreement Technologies Springer 2018 Cham 281 291 978-3-030-01712-5 Lecture Notes in Artificial Intelligence 10767 340 Belardinelli F.