• DocumentCode
    2863610
  • Title

    An approximate Pareto optimal cooperative negotiation model for multiple continuous dependent issues

  • Author

    Gatti, Nicola ; Amigoni, Francesco

  • Author_Institution
    Dipt. di Elettronica e Informazione, Politecnico di Milano, Italy
  • fYear
    2005
  • fDate
    19-22 Sept. 2005
  • Firstpage
    565
  • Lastpage
    571
  • Abstract
    Cooperative negotiation is proved to be an effective paradigm to solve complex dynamic multi-objective problems in which each objective is associated to an agent. When the multi-objective problem is defined on several continuous variables, cooperative negotiation can be traced back to a sequential bargaining. However, the complexity of highly reconfigurable scenarios with a large number of agents does not allow the adoption of classical game theory techniques to design optimal bargaining models for cooperative negotiations. A way to tackle this complexity is based on the decentralization of the system, usually obtained by introducing a mediator that reduces the amount of information directly exchanged between the agents. In this paper we present and experimentally evaluate a decentralized bargaining model for cooperative negotiation on multiple continuous dependent issues able to produce approximate Pareto optimal outcomes.
  • Keywords
    Pareto optimisation; approximation theory; game theory; multi-agent systems; approximate Pareto optimal model; cooperative negotiation; decentralized bargaining model; dynamic multiobjective problems; game theory; multiple continuous dependent issues; sequential bargaining; Ad hoc networks; Airplanes; Computer networks; Design optimization; Distributed computing; Game theory; Performance evaluation; Protocols; Resource management; Stability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Agent Technology, IEEE/WIC/ACM International Conference on
  • Print_ISBN
    0-7695-2416-8
  • Type

    conf

  • DOI
    10.1109/IAT.2005.40
  • Filename
    1565604