bibtype C - Conference Paper (international conference)
ARLID 0555371
utime 20240103230541.6
mtime 20220314235959.9
SCOPUS 85127623905
WOS 000803071400058
DOI 10.1109/IUCC-CIT-DSCI-SmartCNS55181.2021.00073
title (primary) (eng) Agent’s Feedback in Preference Elicitation
specification
page_count 9 s.
media_type E
serial
ARLID cav_un_epca*0555370
ISBN 978-1-6654-6667-7
title International Conference on Ubiquitous Computing and Communications and International Symposium on Cyberspace and Security (IUCC-CSS) 2021
page_num 421-429
publisher
place Piscataway
name IEEE Computer Society
year 2021
keyword Preference elicitation
keyword Adaptive agent
keyword Decision making
keyword Bayes’ rule
author (primary)
ARLID cav_un_auth*0101124
name1 Kárný
name2 Miroslav
institution UTIA-B
full_dept (cz) Adaptivní systémy
full_dept (eng) Department of Adaptive Systems
department (cz) AS
department (eng) AS
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0426627
name1 Siváková
name2 Tereza
institution UTIA-B
full_dept (cz) Adaptivní systémy
full_dept Department of Adaptive Systems
department (cz) AS
department AS
country CZ
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2022/AS/karny-0555371.pdf
cas_special
project
project_id LTC18075
agency GA MŠk
country CZ
ARLID cav_un_auth*0372050
project
project_id CA16228
agency The European Cooperation in Science and Technology (COST)
country XE
ARLID cav_un_auth*0372051
abstract (eng) 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.
action
ARLID cav_un_auth*0426628
name International Conference on Ubiquitous Computing and Communications 2021 (IUCC/CIT/DSCI/SmartCNS 2021) /20./
dates 20211220
mrcbC20-s 20211222
place London
country GB
RIV BC
FORD0 10000
FORD1 10200
FORD2 10201
reportyear 2023
num_of_auth 2
presentation_type PR
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0330292
confidential S
mrcbC86 n.a. Proceedings Paper Computer Science Artificial Intelligence|Computer Science Information Systems|Computer Science Interdisciplinary Applications|Computer Science Theory Methods|Telecommunications
arlyear 2021
mrcbU14 85127623905 SCOPUS
mrcbU24 PUBMED
mrcbU34 000803071400058 WOS
mrcbU63 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