0462888 20240103212627.720160921235959.9 84988039951 000389086300039 10.1007/978-3-319-44778-0_39 8 s. P cav_un_epca*0463079978-3-319-44777-30302-9743Part I.330-338ChamSpringer2016 Games Markov decision process Bayesian learning cav_un_auth*0333671 Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems 25 Hůla František UTIA-B CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0333672 Ruman Marko Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems SK Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101124 Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems 50 Kárný Miroslav UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2016/AS/karny-0462888.pdf cav_un_auth*0292725 GA13-13502S GA ČR 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. cav_un_auth*0333805 International Conference on Artificial Neural Networks 2016 /25./ 20160906 20160909 Barcelona ES BB 2017 3 PR RVO:67985556 http://hdl.handle.net/11104/0262368 S 3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Information Systems|Computer Science Theory Methods|Robotics 0.325 Q2 Q2 2016 84988039951 SCOPUS 000389086300039 WOS 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