• DocumentCode
    1795212
  • Title

    Consensus based on learning game theory

  • Author

    Zhongjie Lin ; Liu, Hugh H. T.

  • Author_Institution
    Univ. of Toronto Inst. for Aerosp. Studies, Toronto, ON, Canada
  • fYear
    2014
  • fDate
    8-10 Aug. 2014
  • Firstpage
    1856
  • Lastpage
    1861
  • Abstract
    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.
  • Keywords
    game theory; learning (artificial intelligence); optimisation; consensus; distributed optimization; fictitious play concept; learning game theory; Algorithm design and analysis; Convergence; Equations; Game theory; Games; Mathematical model; Optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Guidance, Navigation and Control Conference (CGNCC), 2014 IEEE Chinese
  • Conference_Location
    Yantai
  • Print_ISBN
    978-1-4799-4700-3
  • Type

    conf

  • DOI
    10.1109/CGNCC.2014.7007464
  • Filename
    7007464
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1795212