Game-Theoretic Learning for Sensor Reliability Evaluation without Knowledge of the Ground Truth Yazidi, Anis Herrera Viedma, Enrique Unreliable Sensors Identification Game theory Learning automata Sensor fusion 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 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. 2022-01-26T12:00:40Z 2022-01-26T12:00:40Z 2020 info:eu-repo/semantics/article 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] http://hdl.handle.net/10481/72499 eng http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess Atribución-NoComercial-SinDerivadas 3.0 España IEEE