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dc.contributor.authorYazidi, Anis
dc.contributor.authorHammer, Hugo Lewi
dc.contributor.authorSamouylov, Konstantin
dc.contributor.authorHerrera-Viedma, Enrique
dc.date.accessioned2020-02-08T17:24:26Z
dc.date.accessioned2020-02-20T10:11:29Z
dc.date.available2020-02-08T17:24:26Z
dc.date.available2020-02-20T10:11:29Z
dc.date.issued2020
dc.identifier.citationYazidi, Hammer, Samouylov, Herrera-Viedma. Game-Theoretic Learning for Sensor Reliability Evaluation Without Knowledge of the Ground Truth. IEEE Transactions on Cybernetics. 2020en
dc.identifier.issn2168-2267
dc.identifier.issn2168-2267
dc.identifier.issn2168-2275
dc.identifier.urihttps://hdl.handle.net/10642/8150
dc.description.abstractSensor fusion has attracted a lot of research attention during the few last years. Recently, a new research direction has emerged dealing with sensor fusion without knowledge of the ground truth. In this article, 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 results 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. The experimental results show the accuracy of our solution and its ability to deal with settings that are unsolvable by legacy works.en
dc.language.isoenen
dc.publisherIEEE Exploreen
dc.relation.ispartofseriesIEEE Transactions on Cybernetics;Date of Publication 06 January 2020
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectGame theoriesen
dc.subjectLearning automataen
dc.subjectSensor fusionsen
dc.subjectUnreliable sensor identificationsen
dc.titleGame-Theoretic Learning for Sensor Reliability Evaluation Without Knowledge of the Ground Truthen
dc.typeJournal articleen
dc.typePeer revieweden
dc.date.updated2020-02-08T17:24:26Z
dc.description.versionacceptedVersionen
dc.identifier.doihttps://dx.doi.org/10.1109/TCYB.2019.2958616
dc.identifier.cristin1792183
dc.source.journalIEEE Transactions on Cybernetics


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