bibtype C - Conference Paper (international conference)
ARLID 0640202
utime 20251103152839.7
mtime 20251020235959.9
SCOPUS 105016552155
DOI 10.1007/978-3-032-04555-3_29
title (primary) (eng) Targeted Trust-Based Merging of Customers’ Opinions
specification
page_count 15 s.
media_type E
serial
ARLID cav_un_epca*0640201
ISBN 978-3-032-04554-6
ISSN Artificial Neural Networks and Machine Learning – ICANN 2025
title Artificial Neural Networks and Machine Learning – ICANN 2025
part_num 4
page_num 351-365
publisher
place Cham
name Springer
year 2025
editor
name1 Ružejnikov
name2 Jurij
editor
name1 Guy
name2 Tatiana Valentine
keyword Decision-making
keyword Fully probabilistic design
keyword Knowledge representation
keyword Opinion dynamics
keyword Opinion merging
keyword Trust
author (primary)
ARLID cav_un_auth*0491463
name1 Ružejnikov
name2 Jurij
institution UTIA-B
full_dept (cz) Adaptivní systémy
full_dept (eng) Department of Adaptive Systems
department (cz) AS
department (eng) AS
country CZ
share 80
garant K
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101092
name1 Guy
name2 Tatiana Valentine
institution UTIA-B
full_dept (cz) Adaptivní systémy
full_dept Department of Adaptive Systems
department (cz) AS
department AS
full_dept Department of Adaptive Systems
share 20
garant S
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url https://library.utia.cas.cz/separaty/2025/AS/guy-0640202.pdf
cas_special
project
project_id 101168272
agency POSK EU
country XE
ARLID cav_un_auth*0492513
abstract (eng) In this article, we investigate how a rational agent forms their opinion based on prior knowledge, available information, and the opinions of other agents. We propose methodology of how to purposefully merge agent’s opinion and expert opinions. We describe the agent’s opinion and the opinions of experts in the form of distributions. Formulating opinion formation as a decision-making task and solve it using Fully Probabilistic\nDesign (FPD). T o demonstrate our approach, we apply the solution on simulated data describing features of mobile phone brands. Methodology is verified on a test bed example of choosing a mobile phone brand based\non expert opinions while taking into account agent’s trust in experts.
action
ARLID cav_un_auth*0489695
name ICANN 2025: International Conference on Artificial Neural Networks /34./
dates 20250909
mrcbC20-s 20250912
place Kaunas
country LT
RIV BA
FORD0 10000
FORD1 10100
FORD2 10102
reportyear 2026
num_of_auth 2
presentation_type PO
inst_support RVO:67985556
permalink https://hdl.handle.net/11104/0371115
cooperation
ARLID cav_un_auth*0322033
name Česká zemědělská univerzita v Praze, Provozně ekonomická fakulta
institution PEF ČZU
country CZ
confidential S
arlyear 2025
mrcbU14 105016552155 SCOPUS
mrcbU24 PUBMED
mrcbU34 WOS
mrcbU63 cav_un_epca*0640201 Artificial Neural Networks and Machine Learning – ICANN 2025 4 978-3-032-04554-6 0302-9743 351 365 Cham Springer 2025 Lecture Notes in Computer Science 16071
mrcbU67 Ružejnikov Jurij 340
mrcbU67 Guy Tatiana Valentine 340