Agent’s Feedback in Preference Elicitation

0555371 - ÚTIA 2023 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
Kárný, Miroslav - Siváková, Tereza
Agent’s Feedback in Preference Elicitation.
International Conference on Ubiquitous Computing and Communications and International Symposium on Cyberspace and Security (IUCC-CSS) 2021. Piscataway: IEEE Computer Society, 2021, s. 421-429. ISBN 978-1-6654-6667-7.
[International Conference on Ubiquitous Computing and Communications 2021 (IUCC/CIT/DSCI/SmartCNS 2021) /20./. London (GB), 20.12.2021-22.12.2021]
Grant CEP: GA MŠMT(CZ) LTC18075
Grant ostatní: The European Cooperation in Science and Technology (COST)(XE) CA16228
Institucionální podpora: RVO:67985556
Klíčová slova: Preference elicitation * Adaptive agent * Decision making * Bayes’ rule
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://library.utia.cas.cz/separaty/2022/AS/karny-0555371.pdf

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.
Trvalý link: http://hdl.handle.net/11104/0330292