• DocumentCode
    467713
  • Title

    An Extended Contract-Net Negotiation Model Based on Task Coalition and Genetic Algorithm

  • Author

    Tao, Hai-jun ; Wang, Ya-dong ; Guo, Mao-zu

  • Volume
    2
  • fYear
    2007
  • fDate
    19-22 Aug. 2007
  • Firstpage
    879
  • Lastpage
    884
  • Abstract
    Multi-agent negotiation has been one of the key problems in the multi-agent research area. An extended contract-net negotiation model based on task coalition and genetic algorithm is presented after analyzing the advantage and disadvantage of the classical contract-net negotiation model. Formalized definition method and coalition generation algorithm are given. A specialized genetic algorithm, which is optimized by optimized initial colony selection, optimized parent crossover/mutation and the using of Metropolis rule, is used to solve the task allocation in the coalition. The algorithm improves the efficiency of task allocation and reduces the communication cost. By testing and analyzing an example of a missile defense system, it is proved that the model can reduce the negotiation cost effectively contrast with the classical contract-net model on the basis of ensuring the negotiation quality.
  • Keywords
    genetic algorithms; multi-agent systems; Metropolis rule; genetic algorithm; multiagent negotiation; optimized initial colony selection; parent crossover-mutation; task allocation; task coalition; Computational efficiency; Cost function; Cybernetics; Distributed computing; Genetic algorithms; Genetic mutations; Machine learning; Missiles; Multiagent systems; System testing; Generic algorithm; Multi-agent system; Negotiation; Task allocation; Task coalition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2007 International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4244-0973-0
  • Electronic_ISBN
    978-1-4244-0973-0
  • Type

    conf

  • DOI
    10.1109/ICMLC.2007.4370266
  • Filename
    4370266