In this paper, a systematic distributed optimization approach is proposed based on a fictitious play concept. The convergence of the algorithm is proven under the game theory framework. The result is equivalent to a consensus problem. It introduces a novel perspective to study the consensus problem. Such an equivalence is illustrated by numerical cases.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Consensus based on learning game theory


    Beteiligte:
    Lin, Zhongjie (Autor:in) / Liu, Hugh H. T. (Autor:in)


    Erscheinungsdatum :

    01.08.2014


    Format / Umfang :

    164755 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Consensus based on learning game theory with a UAV rendezvous application

    Lin, Zhongjie / Liu, Hugh hong-tao | British Library Online Contents | 2015


    Cooperative Game Theory based Multi-UAV Consensus-based Formation Control

    Jiang, Liwei / Gonzalez, Felipe / McFadyen, Aaron | IEEE | 2020


    Multi-UAV Collaborative Search Decision Making via Consensus-Based Potential Game

    Zhang, Daifeng / Zhang, Jiliang | Springer Verlag | 2025


    Fault-Tolerant Optimal Consensus for Multiagent Systems: A Fuzzy-Based Game Approach

    Yang, Haoyue / Zhang, Hao / Wang, Zhuping et al. | IEEE | 2024


    Game Theory and Reinforcement Learning based Smart Lane Change Strategies

    LU, Gaohui / LI, Jie / wang, Ju et al. | SAE Technical Papers | 2022