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作者单位:František Hůla (16) Marko Ruman (16) Miroslav Kárný (16)
16. Department of Adaptive Systems, Institute of Information Theory and Automation, Czech Academy of Sciences, POB 18, 182 08, Prague 8, Czech Republic
丛书名:Artificial Neural Networks and Machine Learning – ICANN 2016
ISBN:978-3-319-44778-0
刊物类别:Computer Science
刊物主题:Artificial Intelligence and Robotics Computer Communication Networks Software Engineering Data Encryption Database Management Computation by Abstract Devices Algorithm Analysis and Problem Complexity
出版者:Springer Berlin / Heidelberg
ISSN:1611-3349
卷排序:9886
文摘
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.