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dc.contributor.authorKaveh Madani
dc.contributor.authorMilad Hooshyar
dc.contributor.authorSina Khatami
dc.contributor.authorAli Alaeipour
dc.contributor.authorAida Moeini
dc.date.accessioned2017-09-18T10:46:31Z
dc.date.available2017-09-18T10:46:31Z
dc.date.issued2014-01-01
dc.identifier.urihttp://dsp.sbmu.ac.ir/xmlui/handle/123456789/66740
dc.description.abstract© 2014 IEEE. Social (central) planning is normally used in the literature to optimize the system-wide efficiency and utility of multi-operator systems. Central planning tries to maximize system's benefits by coordinating the operators' strategies and reduce the externalities, assuming that all parties are willing to cooperate. This assumption implies that operators are willing to base their decisions based on group rationality rather than individual rationality, even if increased group benefits results in reduced benefits for some agents. This assumption limits the applicability of social planner's solutions, as perfect cooperation among agents is often infeasible in real world. Recognizing the fact that decisions are normally based on individual rationality in human systems, cooperative game theory methods are normally employed to address the major limitation of social planner's methods. Game theory methods revise the social planner's solution such that not only group benefits are increased, but also there exists no agent whose cooperative gain is less than his noncooperative gain. However, in most cases, utility is assumed to be transferrable and the literature has not sufficiently focused on non-transferrable utility games. In such games parties are willing to cooperate and coordinate their strategies to increase their benefits, but have no ability to compensate each other to promote cooperation. To a good extent, the transferrable utility assumption is due to the complexity of calculations to find the best response strategies of agents in non-cooperative and cooperative modes, especially in multi-period games. By combining Reinforcement Learning and Nash bargaining solution, this paper develops a new method for applying cooperative game theory to complex multi-period nontransferrable utility games. For illustration, the suggested method is applied to two numerical examples in which two hydropower operators seek developing a fair and efficient cooperation mechanism to increase their gains.
dc.sourceConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
dc.subjectAgent-based modeling (ABM)
dc.subjectGame theory
dc.subjectReinforcement learning
dc.subjectReservoir operation
dc.subjectWater resources
dc.titleNash-reinforcement learning (N-RL) for developing coordination strategies in non- Transferable utility games
dc.journal.volume2014-January
dc.journal.issueJanuary
dc.identifier.doi10.1109/smc.2014.6974336
dc.journal.pages2705-2710
dc.contributor.authorid25960225600
dc.contributor.authorid56342251100
dc.contributor.authorid56740552100
dc.contributor.authorid56741505900
dc.contributor.authorid55343824700
dc.contributor.citation25960225600|60015150|Kaveh Madani
dc.contributor.citation56342251100|60022144|Milad Hooshyar
dc.contributor.citation56740552100|60026553|Sina Khatami
dc.contributor.citation56741505900|101930171|Ali Alaeipour
dc.contributor.citation55343824700|60018934|Aida Moeini
dc.contributor.affiliationid60015150
dc.contributor.affiliationid60022144
dc.contributor.affiliationid60026553
dc.contributor.affiliationid101930171
dc.contributor.affiliationid60018934


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