0555371 20240103230541.620220314235959.9 85127623905 000803071400058 10.1109/IUCC-CIT-DSCI-SmartCNS55181.2021.00073 9 s. E cav_un_epca*0555370978-1-6654-6667-7421-429PiscatawayIEEE Computer Society2021 Preference elicitation Adaptive agent Decision making Bayes’ rule cav_un_auth*0101124 Kárný Miroslav UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS 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/2022/AS/karny-0555371.pdf LTC18075 GA MŠk CZ cav_un_auth*0372050 CA16228 The European Cooperation in Science and Technology (COST) XE cav_un_auth*0372051 A generic decision-making (DM) agent specifies its preferences partially. The studied prescriptiveDMtheory, called fully probabilistic design (FPD) of decision strategies, has recently addressed this obstacle in a new way. The found preference completion and quantification exploits that: IFPD models the closed DM loop and the agent’s preferences by joint probability densities (pds), Ithere is a preference-elicitation (PE) principle, which maps the agent’s model of the state transitions and its incompletely expressed wishes on an ideal pd quantifying them. The gained algorithmic uantification provides ambitious but potentially reachable DM aims. It suppresses demands on the agent selecting the preference-expressing inputs. The remaining PE options are: Ia parameter balancing exploration with exploitation, Ia fine specification of the ideal (desired) sets of states and actions, Irelative importance of these ideal sets. The current paper makes decisive steps towards a systematic and realistic choice of such inputs by solving a meta-DM task. The algorithmic “meta-agent” observes the user’s satisfaction, expressed by school-type marks, and tunes the free PE inputs to improve these marks. The solution requires a suitable formalisation of such a meta-task. This is done here. The proposed way copes with the danger of infinite regress and the imensionality curse. Non-trivial simulations illustrate the results. cav_un_auth*0426628 International Conference on Ubiquitous Computing and Communications 2021 (IUCC/CIT/DSCI/SmartCNS 2021) /20./ 20211220 20211222 London GB BC 10000 10200 10201 2023 2 PR RVO:67985556 http://hdl.handle.net/11104/0330292 S 3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Information Systems|Computer Science Interdisciplinary Applications|Computer Science Theory Methods|Telecommunications 2021 85127623905 SCOPUS PUBMED 000803071400058 WOS cav_un_epca*0555370 International Conference on Ubiquitous Computing and Communications and International Symposium on Cyberspace and Security (IUCC-CSS) 2021 978-1-6654-6667-7 421 429 Piscataway IEEE Computer Society 2021