0573588 20240402214144.620230717235959.9 85166665633 001039370200001 10.1016/j.automatica.2023.111185 8 s. P cav_un_epca*02562180005-1098156Elsevier Dynamic performance Probabilistic Preferences Optimal strategy Preference elicitation Exploration 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*0426627 Siváková Tereza UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS CZ Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2023/AS/karny-0573588-preprint.pdf https://www.sciencedirect.com/science/article/pii/S0005109823003461?via%3Dihub CA21169 EU-COST XE cav_un_auth*0452289 Any prescriptive theory of decision-making (DM) has to cope with the common DM agents’ inability to fully specify their preferences dependent on several attributes. The paper provides the needed preference completion and quantification for fully probabilistic design (FPD) of DM strategies. FPD (covering the usual Bayesian DM) probabilistically models the agent’s environment and quantifies its preferences via an ideal probabilistic model of the closed DM loop. The probability density (pd) models (closed-loop) behaviour, a collection of involved random variables. Its ideal twin is high on desired behaviours, small on undesired and zero on forbidden ones. The FPD-optimal strategy minimises the Kullback-Leibler divergence (KLD) of the closed-loop modelling pd to the ideal twin. The exposed preference quantification chooses the optimal ideal pd from the set of pds compatible with partially-specified agent’s preferences. The optimal ideal pd minimises the KLD minima reached by the optimal strategies for respective imminent ideal pds. This preference-focused twin of the minimum KLD principle was applied to special sets of ideal pds. The paper extends them towards exploration and balancing contradictory wishes on states and actions. WOS IN 20000 20200 20205 2024 RVO:67985556 https://hdl.handle.net/11104/0344021 S 111185 Article Automation Control Systems|Engineering Electrical Electronic C AUTOMATION&CONTROLSYSTEMS|ENGINEERING.ELECTRICAL&ELECTRONIC 6 0.9 8.3 0.05451 2.323 48545 331 3.502 33.15 5.47 10764 Q1 4.500 530 Q1 78.6 Q1* Q1* 1.24 Q1 80.3 2023 85166665633 SCOPUS PUBMED 001039370200001 WOS cav_un_epca*0256218 Automatica Roč. 156 č. 1 2023 0005-1098 1873-2836 Elsevier