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Solving Leaderless Multi-Cluster Games over Directed Graphs

Zimmermann, Jan ; Tatarenko, Tatiana ; Willert, Volker ; Adamy, Jürgen (2021)
Solving Leaderless Multi-Cluster Games over Directed Graphs.
In: European Journal of Control, 62
doi: 10.1016/j.ejcon.2021.06.007
Artikel, Bibliographie

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Kurzbeschreibung (Abstract)

We are concerned with finding Nash Equilibria in agent-based multi-cluster games, where agents are separated into distinct clusters. While the agents inside each cluster collaborate to achieve a common goal, the clusters themselves are considered to be virtual players that compete against each other in a non-cooperative game with respect to a coupled cost function. In such scenarios, the intra-cluster problem and the game between the clusters need to be solved simultaneously. Therefore, the resulting inter-cluster Nash Equilibrium should also be a minimizer of the social welfare problem inside the clusters. In this work, this setup is cast as a distributed optimization problem with sparse state information. Hence, critical information, such as the agent’s cost functions, remains private. We present a distributed algorithm that converges with a linear rate to the optimal solution. Furthermore, we apply our algorithm to an extended Cournot game to verify our theoretical results.

Typ des Eintrags: Artikel
Erschienen: 2021
Autor(en): Zimmermann, Jan ; Tatarenko, Tatiana ; Willert, Volker ; Adamy, Jürgen
Art des Eintrags: Bibliographie
Titel: Solving Leaderless Multi-Cluster Games over Directed Graphs
Sprache: Englisch
Publikationsjahr: November 2021
Verlag: Elsevier
Titel der Zeitschrift, Zeitung oder Schriftenreihe: European Journal of Control
Jahrgang/Volume einer Zeitschrift: 62
DOI: 10.1016/j.ejcon.2021.06.007
URL / URN: https://www.sciencedirect.com/science/article/abs/pii/S09473...
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Kurzbeschreibung (Abstract):

We are concerned with finding Nash Equilibria in agent-based multi-cluster games, where agents are separated into distinct clusters. While the agents inside each cluster collaborate to achieve a common goal, the clusters themselves are considered to be virtual players that compete against each other in a non-cooperative game with respect to a coupled cost function. In such scenarios, the intra-cluster problem and the game between the clusters need to be solved simultaneously. Therefore, the resulting inter-cluster Nash Equilibrium should also be a minimizer of the social welfare problem inside the clusters. In this work, this setup is cast as a distributed optimization problem with sparse state information. Hence, critical information, such as the agent’s cost functions, remains private. We present a distributed algorithm that converges with a linear rate to the optimal solution. Furthermore, we apply our algorithm to an extended Cournot game to verify our theoretical results.

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Erstveröffentlichung

Fachbereich(e)/-gebiet(e): 18 Fachbereich Elektrotechnik und Informationstechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik > Regelungsmethoden und Robotik (ab 01.08.2022 umbenannt in Regelungsmethoden und Intelligente Systeme)
Hinterlegungsdatum: 12 Jul 2021 08:11
Letzte Änderung: 22 Jul 2024 12:51
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