0519795 20241106135817.420200115235959.9 85081588754 000507495600041 10.1016/j.ifacol.2019.12.656 6 s. E cav_un_epca*05197942405-8963239-244AmsterdamElsevier2019 dynamic decision making Kullback Leibler Divergence decision strategy fully probabilistic design preference elicitation cav_un_auth*0101124 Kárný Miroslav 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*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. http://library.utia.cas.cz/separaty/2019/AS/karny-0519795.pdf cav_un_auth*0372050 LTC18075 GA MŠk CZ cav_un_auth*0372051 CA16228 EU-COST XE The paper proposes the preference-elicitation support within the framework of fully probabilistic design (FPD) of decision strategies. Agent employing FPD uses probability densities to model the closed-loop behaviour, i.e. a collection of all observed, opted and considered random variables. Opted actions are generated by a randomised strategy. The optimal decision strategy minimises KullbackLeibler divergence of the closed-loop model to its ideal counterpart describing the agent’s preferences. Thus, selecting the ideal closed-loop model comprises preference elicitation. The paper provides a general choice of the best ideal closed-loop model reflecting agent’s preferences. The foreseen application potential of such a preference elicitation is high as FPD is a non-trivial dense extension of Bayesian decision making that dominates prescriptive decision theories. The general solution is illustrated on the regulation task with a linear Gaussian model describing the agent’s environment. cav_un_auth*0387490 IFAC Workshop on Adaptive and Learning Control Systems 2019 /13./ 20191204 20191206 Winchester GB BD 10000 10100 10103 2020 4 A sml 4as 20241106135817.4 PR RVO:67985556 http://hdl.handle.net/11104/0304785 S Copyright form 20191107 2 Article Biotechnology Applied Microbiology|Microbiology 0.260 Q3 2019 Soubory v repozitáři: karny-0519795 -ALCOS19_CopyrightForm_57.pdf 85081588754 SCOPUS PUBMED 000507495600041 WOS cav_un_epca*0519794 IFAC-PapersOnLine. Volume 52, Issue 29 - Proceedings of the 13th IFAC Workshop on Adaptive and Learning Control Systems 2019 2405-8963 239 244 Amsterdam Elsevier 2019