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Game-Theoretic Learning for Sensor Reliability Evaluation without Knowledge of the Ground Truth
dc.contributor.author | Yazidi, Anis | |
dc.contributor.author | Herrera Viedma, Enrique | |
dc.date.accessioned | 2022-01-26T12:00:40Z | |
dc.date.available | 2022-01-26T12:00:40Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Published version: A. Yazidi... [et al.]. "Game-Theoretic Learning for Sensor Reliability Evaluation Without Knowledge of the Ground Truth," in IEEE Transactions on Cybernetics, vol. 51, no. 12, pp. 5706-5716, Dec. 2021, doi: [10.1109/TCYB.2019.2958616] | es_ES |
dc.identifier.uri | http://hdl.handle.net/10481/72499 | |
dc.description | The authors would like to thank FEDER financial supp011 from the Project TIN2016-75850-P. lt also was supported by the RUDN University Program 5-100 | es_ES |
dc.description.abstract | Sensor fusion has attracted a lot of research attention during the last years. Recently, a new research direction has emerged dealing with sensor fusion without knowledge of the ground truth. In this paper, we present a novel solution to the latter pertinent problem. In contrast to the first reported solutions to this problem, we present a solution that does not involve any assumption on the group average reliability which makes our 1·esults more general than previous works. We devise a strategic game where we show that a perfect partitioning of the sensors into reliable and unreliable groups corresponds to a Nash equilibrium of the game. Furthermore, we give sound theoretical results that prove that those equilibria are indeed the unique Nash equilibria of the game. We then propose a solution involving a team of Learning Automata (LA) to unveil the identity of each sensor, whether it is reliable or unreliable, using game-theoretic learning. Experimental results show the accuracy of our solution and its ability to deal with settings that are unsolvable by legacy works. | es_ES |
dc.description.sponsorship | European Commission TIN2016-75850-P | es_ES |
dc.description.sponsorship | RUDN University Program 5-100 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | IEEE | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Unreliable Sensors Identification | es_ES |
dc.subject | Game theory | es_ES |
dc.subject | Learning automata | es_ES |
dc.subject | Sensor fusion | es_ES |
dc.title | Game-Theoretic Learning for Sensor Reliability Evaluation without Knowledge of the Ground Truth | es_ES |
dc.type | journal article | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.type.hasVersion | AM | es_ES |