bibtype C - Conference Paper (international conference)
ARLID 0575198
utime 20240402214354.0
mtime 20230906235959.9
title (primary) (eng) Experiments with the User’s Feedback in Preference Elicitation
specification
page_count 13 s.
media_type E
serial
ARLID cav_un_epca*0575199
ISSN AIABI-2022 : Artificial Intelligence and Applications for Business and Industries 2022
title AIABI-2022 : Artificial Intelligence and Applications for Business and Industries 2022
publisher
place Achen
name CEUR-WS
year 2023
keyword Preference elicitation
keyword Adaptive agent
keyword Decision making
keyword Bayes rule
author (primary)
ARLID cav_un_auth*0426627
name1 Siváková
name2 Tereza
institution UTIA-B
full_dept (cz) Adaptivní systémy
full_dept (eng) Department of Adaptive Systems
department (cz) AS
department (eng) AS
country CZ
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101124
name1 Kárný
name2 Miroslav
institution UTIA-B
full_dept (cz) Adaptivní systémy
full_dept Department of Adaptive Systems
department (cz) AS
department AS
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
source_type pdf
url http://library.utia.cas.cz/separaty/2023/AS/sivakova-0575198.pdf
cas_special
abstract (eng) This paper deals with user’s preferences (wishes). Common users are uneducated in the decision-making (DM) theory and present their preferences incompletely. That is why we elicit them from such a user during the DM. The paper works with the DM theory called fully probabilistic design (FPD). FPD models closed DM loop, made by the user and the system, by the joint probability density (pd, real pd). A joint ideal pd quantifies the user’s preferences. It assigns high probability values to preferred closed-loop behaviors and low values to undesired behaviors. The real pd should be kept near the ideal pd. By minimizing the Kullback-Leibler divergence of the real and ideal pds, the optimal decision policy is found. The presented algorithmic quantification of preferences provides ambitious but potentially reachable DM aims. It suppresses demands on tuning preference-expressing parameters. The considered ideal pd assigns high probabilities to desired (ideal) sets of states and actions. The parameters of the ideal pd (tuned during the DM via the user’s feedback) are: ▶ relative significance of respective probabilities. ▶ a parameter balancing exploration with exploitation. Their systematic tuning solves meta-DM level task, which observes the agent’s satisfaction expressed humanly by “school-marks”. It opts free parameters to reach the best marks. A formalization and solution of this meta-task were recently done, but experience with it is limited. This paper recalls the theory and provides representative samples of extensive up to now missing simulations.\n
action
ARLID cav_un_auth*0454099
name Artificial Intelligence and Applications for Business and Industries 2022
dates 20221127
mrcbC20-s 20221202
place Udine
country IT
RIV BC
FORD0 10000
FORD1 10200
FORD2 10201
reportyear 2024
num_of_auth 2
presentation_type PR
inst_support RVO:67985556
permalink https://hdl.handle.net/11104/0345034
mrcbC61 1
confidential S
arlyear 2023
mrcbU14 SCOPUS
mrcbU24 PUBMED
mrcbU34 WOS
mrcbU56 pdf
mrcbU63 cav_un_epca*0575199 AIABI-2022 : Artificial Intelligence and Applications for Business and Industries 2022 CEUR-WS 2023 Achen ceur-ws.org 3463 1613-0073