• DocumentCode
    2316800
  • Title

    Coalition formation through learning in autonomic networks

  • Author

    Jiang, Tao ; Baras, John S.

  • Author_Institution
    Inst. for Syst. Res., Univ. of Maryland, College Park, MD, USA
  • fYear
    2009
  • fDate
    13-15 May 2009
  • Firstpage
    10
  • Lastpage
    16
  • Abstract
    Autonomic networks rely on the cooperation of participating nodes for almost all their functions. However, due to resource constraints, nodes are generally selfish and try to maximize their own benefit when participating in the network. Therefore, it is important to study mechanisms, which can be used as incentives for cooperation inside the network. In this paper, the interactions among nodes are modelled as games. A node joins a coalition if it decides to cooperate with at least one node in the coalition. The dynamics of coalition formation proceed via nodes that interact strategically and adapt their behavior to the observed behavior of others. We present conditions that the coalition formed is stable in terms of Nash stability and the core of the coalitional game.
  • Keywords
    game theory; learning (artificial intelligence); telecommunication networks; Nash stability; autonomic network; coalition formation; coalitional game; learning; resource constraint; Collaborative work; Communication networks; Communication system control; Costs; Educational institutions; Game theory; Nash equilibrium; Routing; Spread spectrum communication; Stability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Game Theory for Networks, 2009. GameNets '09. International Conference on
  • Conference_Location
    Istanbul
  • Print_ISBN
    978-1-4244-4176-1
  • Electronic_ISBN
    978-1-4244-4177-8
  • Type

    conf

  • DOI
    10.1109/GAMENETS.2009.5137377
  • Filename
    5137377