0543581 20240103225949.420210701235959.9 000663694300001 85108291105 10.1007/s13042-021-01358-w 15 s. P cav_un_epca*04610481868-8071129 (2021)2717-2731Springer Bayesian learning Adaptive agent Meta-parameter tuning Fully probabilistic design Kullback–Leibler divergence Dynamic decision making 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. http://library.utia.cas.cz/separaty/2021/AS/karny-0543581.pdf https://link.springer.com/article/10.1007/s13042-021-01358-w LTC18075 GA MŠk CZ cav_un_auth*0372050 CA16228 The European Cooperation in Science and Technology (COST) XE cav_un_auth*0372051 A decision-making (DM) agent models its environment and quantifes its DM preferences. An adaptive agent models them locally nearby the realisation of the behaviour of the closed DM loop. Due to this, a simple tool set often sufces for solving complex dynamic DM tasks. The inspected Bayesian agent relies on a unifed learning and optimisation framework, which works well when tailored by making a range of case-specifc options. Many of them can be made of-line. These options concern the sets of involved variables, the knowledge and preference elicitation, structure estimation, etc. Still, some metaparameters need an on-line choice. This concerns, for instance, a weight balancing exploration with exploitation, a weight refecting agent’s willingness to cooperate, a discounting factor, etc. Such options infuence, often vitally, DM quality and their adaptive tuning is needed. Specifc ways exist, for instance, a data-dependent choice of a forgetting factor serving to tracking of parameter changes. A general methodology is, however, missing. The paper opens a pathway to it. The solution uses a hierarchical feedback exploiting a generic, DM-related, observable, mismodelling indicator. The paper presents and justifes the theoretical concept, outlines and illustrates its use. WOS BC 20000 20200 20205 2022 4 A sml 4as 20231122145815.1 RVO:67985556 http://hdl.handle.net/11104/0320766 S Licence to Publish 20210528 3+4 Article Computer Science Artificial Intelligence C COMPUTERSCIENCE.ARTIFICIALINTELLIGENCE 3.764 0.637 2.7 0.0051 0.597 4115 73 1.003 46.47 4.59 2590 Q2 3.862 234 Q2 61 Q3 Q2 0.85 Q2 61.034 2021 Soubory v repozitáři: karny-0543581-licence-IJMLC.pdf 85108291105 SCOPUS PUBMED 000663694300001 WOS cav_un_epca*0461048 International Journal of Machine Learning and Cybernetics 1868-8071 1868-808X Roč. 12 č. 9 2021 2717 2731 Springer