| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0462888 |
| utime |
20240103212627.7 |
| mtime |
20160921235959.9 |
| SCOPUS |
84988039951 |
| WOS |
000389086300039 |
| DOI |
10.1007/978-3-319-44778-0_39 |
| title
(primary) (eng) |
Adaptive Proposer for Ultimatum Game |
| specification |
| page_count |
8 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0463079 |
| ISBN |
978-3-319-44777-3 |
| ISSN |
0302-9743 |
| title
|
Artificial Neural Networks and Machine Learning – ICANN 2016 |
| part_num |
Part I. |
| page_num |
330-338 |
| publisher |
| place |
Cham |
| name |
Springer |
| year |
2016 |
|
|
| keyword |
Games |
| keyword |
Markov decision process |
| keyword |
Bayesian learning |
| author
(primary) |
| ARLID |
cav_un_auth*0333671 |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| full_dept |
Department of Adaptive Systems |
| share |
25 |
| name1 |
Hůla |
| name2 |
František |
| institution |
UTIA-B |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0333672 |
| name1 |
Ruman |
| name2 |
Marko |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| institution |
UTIA-B |
| full_dept |
Department of Adaptive Systems |
| country |
SK |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101124 |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| full_dept |
Department of Adaptive Systems |
| share |
50 |
| name1 |
Kárný |
| name2 |
Miroslav |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0292725 |
| project_id |
GA13-13502S |
| agency |
GA ČR |
|
| abstract
(eng) |
Ultimate Game serves for extensive studies of various aspects of human decision making. The current paper contribute to them by designing proposer optimising its policy using Markov-decision-process (MDP) framework combined with recursive Bayesian learning of responder’s model. Its foreseen use: i) standardises experimental conditions for studying rationality and emotion-influenced decision making of human responders; ii) replaces the classical game-theoretical design of the players’ policies by an adaptive MDP, which is more realistic with respect to the knowledge available to individual players and decreases player’s deliberation effort; iii) reveals the need for approximate learning and dynamic programming inevitable for coping with the curse of dimensionality; iv) demonstrates the influence of the fairness attitude of the proposer on the game course; v) prepares the test case for inspecting exploration-exploitation dichotomy. |
| action |
| ARLID |
cav_un_auth*0333805 |
| name |
International Conference on Artificial Neural Networks 2016 /25./ |
| dates |
20160906 |
| mrcbC20-s |
20160909 |
| place |
Barcelona |
| country |
ES |
|
| RIV |
BB |
| reportyear |
2017 |
| num_of_auth |
3 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0262368 |
| confidential |
S |
| mrcbC86 |
3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Information Systems|Computer Science Theory Methods|Robotics |
| mrcbT16-s |
0.325 |
| mrcbT16-4 |
Q2 |
| mrcbT16-E |
Q2 |
| arlyear |
2016 |
| mrcbU14 |
84988039951 SCOPUS |
| mrcbU34 |
000389086300039 WOS |
| mrcbU63 |
cav_un_epca*0463079 Artificial Neural Networks and Machine Learning – ICANN 2016 Part I. 978-3-319-44777-3 0302-9743 330 338 Cham Springer 2016 Lecture Notes in Computer Science 9886 |
|