The Emergence of Cooperation in a Society of Autonomous Agents   [AA]

by

Ito, A. and Yano, H.

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Info: Proceedings of the First International Conference on Multi--Agent Systems (Conference proceedings), 1995, p. 201-208
Abstract:
The emergence of cooperation in a society of autonomous agents [AA] is investigated. Each agent is made to repetitively engage in a deal equivalent to the ``Prisoner's Dilemma'' game, each time changing the other party of the deal. The conditions of the deal are that the contract histories of all the agents are disclosed to the public. Several deal strategies are evaluated, and their behaviors are investigated by matching them under various conditions. Next the social evolution [SE] of deal strategies is investigated using genetic algorithm techniques. [GA] Each agent can bear a child according to the profit he gets through the deal. The child inherits the deal strategy of the parent, but the random mutation is introduced to the inheritance of strategies. It is shown that the robust and cooperative strategies emerges through the evolution starting from a simple ``Tit [TFT] for Tat'' algorithms.
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BibTex:
@InProceedings{Ito:1995:ECS,
  title =        "The Emergence of Cooperation in a Society of
                 Autonomous Agents",
  author =       "Akira Ito and Hiroyuki Yano",
  pages =        "201--208",
  booktitle =    "Proceedings of the First International Conference on
                 Multi--Agent Systems",
  year =         "1995",
  publisher =    "MIT Press",
  address =      "San Francisco, CA",
  editor =       "Victor Lesser",
  abstract =     "The emergence of cooperation in a society of
                 autonomous agents is investigated. Each agent is made
                 to repetitively engage in a deal equivalent to the
                 ``Prisoner's Dilemma'' game, each time changing the
                 other party of the deal. The conditions of the deal are
                 that the contract histories of all the agents are
                 disclosed to the public. Several deal strategies are
                 evaluated, and their behaviors are investigated by
                 matching them under various conditions. Next the social
                 evolution of deal strategies is investigated using
                 genetic algorithm techniques. Each agent can bear a
                 child according to the profit he gets through the deal.
                 The child inherits the deal strategy of the parent, but
                 the random mutation is introduced to the inheritance of
                 strategies. It is shown that the robust and cooperative
                 strategies emerges through the evolution starting from
                 a simple ``Tit for Tat'' algorithms.",
}