bibtype J - Journal Article
ARLID 0562376
utime 20240103230926.7
mtime 20221014235959.9
SCOPUS 85139393431
WOS 000866441700001
DOI 10.1109/ACCESS.2022.3210506
title (primary) (eng) Indirect Dynamic Negotiation in the Nash Demand Game
specification
page_count 14 s.
media_type E
serial
ARLID cav_un_epca*0461036
ISSN 2169-3536
title IEEE Access
volume_id 10
volume 1 (2022)
page_num 105008-105021
publisher
name Institute of Electrical and Electronics Engineers
keyword Learning systems
keyword Bayes methods
keyword Markov processes
keyword Biological system modeling
keyword Uncertainty
keyword Nash equilibrium
keyword Resource management
author (primary)
ARLID cav_un_auth*0437960
name1 Guy
name2 T. V.
country CZ
share 50
author
ARLID cav_un_auth*0202365
name1 Homolová
name2 Jitka
institution UTIA-B
full_dept (cz) Adaptivní systémy
full_dept Department of Adaptive Systems
department (cz) AS
department AS
country CZ
share 40
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0437961
name1 Gaj
name2 A.
country CZ
share 10
source
url http://library.utia.cas.cz/separaty/2022/AS/homolova-0562376.pdf
source
url https://ieeexplore.ieee.org/document/9905577
cas_special
project
project_id LTC18075
agency GA MŠk
country CZ
ARLID cav_un_auth*0372050
abstract (eng) The paper addresses a problem of sequential bilateral bargaining with incomplete information. We proposed a decision model that helps agents to successfully bargain by performing indirect negotiation and learning the opponent’s model. Methodologically the paper casts heuristically-motivated bargaining of a self-interested independent player into a framework of Bayesian learning and Markov decision processes. The special form of the reward implicitly motivates the players to negotiate indirectly, via closed-loop interaction. We illustrate the approach by applying our model to the Nash demand game, which is an abstract model of bargaining. The results indicate that the established negotiation: i) leads to coordinating players’ actions. ii) results in maximising success rate of the game and iii) brings more individual profit to the players.
result_subspec WOS
RIV BC
FORD0 20000
FORD1 20200
FORD2 20205
reportyear 2023
num_of_auth 3
inst_support RVO:67985556
permalink https://hdl.handle.net/11104/0334712
cooperation
ARLID cav_un_auth*0299698
name Česká zemědělská univerzita Praha
institution ČZU
country CZ
cooperation
ARLID cav_un_auth*0329918
name FJFI ČVUT Praha
country CZ
confidential S
mrcbC86 n.a. Article Computer Science Information Systems|Engineering Electrical Electronic|Telecommunications
mrcbC91 A
mrcbT16-e COMPUTERSCIENCEINFORMATIONSYSTEMS|ENGINEERINGELECTRICALELECTRONIC|TELECOMMUNICATIONS
mrcbT16-j 0.685
mrcbT16-s 0.926
mrcbT16-D Q3
mrcbT16-E Q2
arlyear 2022
mrcbU14 85139393431 SCOPUS
mrcbU24 PUBMED
mrcbU34 000866441700001 WOS
mrcbU63 cav_un_epca*0461036 IEEE Access 2169-3536 2169-3536 Roč. 10 č. 1 2022 105008 105021 Institute of Electrical and Electronics Engineers