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Two-Sided Parameter Learning of Role Selections for Efficient Team Formation

  • Dai Hamada
  • Toshiharu Sugawara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7455)

Abstract

We propose a method of learning to determine appropriate roles for forming efficiently teams by self-interested agents in task-oriented domains. Service requests on computer networks have recently been rapidly increasing. To improve the performance of such systems, issues with efficient team formation to do tasks have attracted our interest. The main feature of the proposed method is learning from two-sided viewpoints, i.e., team leaders who have the initiative to form teams or team members who work in one of the teams that are solicited. For this purpose, we introduce three parameters to agents so that they can select their roles of being a leader or a member. Then, an agent can anticipate what other agents should be selected as team members and what team it should join. Our experiments demonstrated that the amount of utility earned as the result of successful team formation was considerably larger than that with a conventional method. We also conducted a number of experiments to investigate the characteristics of the proposed method. The results revealed that the divisional cooperation between agents was developed, which could reduce the chance of conflicts in decisions to play roles and this achieved efficient team formation.

Keywords

Multiagent System Autonomous Agent Coalition Formation Success Ratio Random Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dai Hamada
    • 1
  • Toshiharu Sugawara
    • 1
  1. 1.Department of Computer Science and EngineeringWaseda UniversityTokyoJapan

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