Agent’s Feedback in Preference Elicitation

0555371 - ÚTIA 2023 RIV US eng C - Conference Paper (international conference)
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]
R&D Projects: GA MŠMT(CZ) LTC18075
Grant - others:The European Cooperation in Science and Technology (COST)(XE) CA16228
Institutional support: RVO:67985556
Keywords : Preference elicitation * Adaptive agent * Decision making * Bayes’ rule
OECD category: 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.
Permanent Link: http://hdl.handle.net/11104/0330292