0353209 20251120160000.720110104235959.9 000295049106014 10.1109/CDC.2010.5717087 6 s. cav_un_epca*0353206978-1-4244-7745-60743-15465327-5332AtlantaIEEE2010 knowledge elicitation Bayesian decision making fullz probabilistic design cav_un_auth*0101124 Kárný Miroslav Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101092 Guy Tatiana Valentine Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2010/AS/karny-preference elicitation in fully probabilistic design of decision strategies.pdf GA102/08/0567 GA ČR cav_un_auth*0239566 CEZ:AV0Z10750506 Any systematic decision-making design selects a decision strategy that makes the resulting closed-loop behaviour close to the desired one. Fully Probabilistic Design (FPD) describes modelled and desired closed-loop behaviours via their distributions. The designed strategy is a minimiser of Kullback-Leibler divergence of these distributions. FPD: i) unifies modelling and aim-expressing languages; ii) directly describes multiple aims and constraints; iii) simplifies an (inevitable) approximate design as it has an explicit minimiser. The paper enriches the theory of FPD, in particular, it: i) improves its axiomatic basis; ii) quantitatively relates FPD to standard Bayesian decision making showing that the set of FPD tasks is a dense extension of Bayesian problem formulations; iii) opens a way to a systematic data-based preference elicitation, i.e., quantitative expression of decision-making aims. cav_un_auth*0267820 49th IEEE Conference on Decision and Control Atlanta 14.12.2010-18.12.2010 US 2011 BB http://hdl.handle.net/11104/0006221 2010 000295049106014 WOS cav_un_epca*0353206 Proceedings of the 49th IEEE Conference on Decision and Control 978-1-4244-7745-6 0743-1546 5327 5332 Atlanta IEEE 2010