Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/201110
Full metadata record
DC FieldValueLanguage
dc.contributor.authorXuanyu Caoen_US
dc.contributor.authorK. J. Ray Liuen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:48:43Z-
dc.date.available2020-04-06T07:48:43Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/LSP.2017.2693819en_US
dc.identifier.urihttp://localhost/handle/Hannan/201110-
dc.description.abstractIn this work, we study the social learning problem, in which agents of a networked system collaborate to detect the state of the nature based on their private signals. A novel distributed graphical evolutionary game-theoretic learning method is proposed. In the proposed game-theoretic method, agents only need to communicate their binary decisions rather than the real-valued beliefs with their neighbors, which endows the method with low communication complexity. Under mean field approximations, we theoretically analyze the steady-state equilibria of the game and show that the evolutionarily stable states coincide with the decisions of the benchmark centralized detector. Numerical experiments are implemented to confirm the effectiveness of the proposed game-theoretic learning method.en_US
dc.format.extent765,en_US
dc.format.extent769en_US
dc.publisherIEEEen_US
dc.relation.haspart7898499.pdfen_US
dc.titleA Graphical Evolutionary Game Approach to Social Learningen_US
dc.typeArticleen_US
dc.journal.volume24en_US
dc.journal.issue6en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7898499.pdf229.6 kBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorXuanyu Caoen_US
dc.contributor.authorK. J. Ray Liuen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:48:43Z-
dc.date.available2020-04-06T07:48:43Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/LSP.2017.2693819en_US
dc.identifier.urihttp://localhost/handle/Hannan/201110-
dc.description.abstractIn this work, we study the social learning problem, in which agents of a networked system collaborate to detect the state of the nature based on their private signals. A novel distributed graphical evolutionary game-theoretic learning method is proposed. In the proposed game-theoretic method, agents only need to communicate their binary decisions rather than the real-valued beliefs with their neighbors, which endows the method with low communication complexity. Under mean field approximations, we theoretically analyze the steady-state equilibria of the game and show that the evolutionarily stable states coincide with the decisions of the benchmark centralized detector. Numerical experiments are implemented to confirm the effectiveness of the proposed game-theoretic learning method.en_US
dc.format.extent765,en_US
dc.format.extent769en_US
dc.publisherIEEEen_US
dc.relation.haspart7898499.pdfen_US
dc.titleA Graphical Evolutionary Game Approach to Social Learningen_US
dc.typeArticleen_US
dc.journal.volume24en_US
dc.journal.issue6en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7898499.pdf229.6 kBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorXuanyu Caoen_US
dc.contributor.authorK. J. Ray Liuen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:48:43Z-
dc.date.available2020-04-06T07:48:43Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/LSP.2017.2693819en_US
dc.identifier.urihttp://localhost/handle/Hannan/201110-
dc.description.abstractIn this work, we study the social learning problem, in which agents of a networked system collaborate to detect the state of the nature based on their private signals. A novel distributed graphical evolutionary game-theoretic learning method is proposed. In the proposed game-theoretic method, agents only need to communicate their binary decisions rather than the real-valued beliefs with their neighbors, which endows the method with low communication complexity. Under mean field approximations, we theoretically analyze the steady-state equilibria of the game and show that the evolutionarily stable states coincide with the decisions of the benchmark centralized detector. Numerical experiments are implemented to confirm the effectiveness of the proposed game-theoretic learning method.en_US
dc.format.extent765,en_US
dc.format.extent769en_US
dc.publisherIEEEen_US
dc.relation.haspart7898499.pdfen_US
dc.titleA Graphical Evolutionary Game Approach to Social Learningen_US
dc.typeArticleen_US
dc.journal.volume24en_US
dc.journal.issue6en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7898499.pdf229.6 kBAdobe PDF