Deciding Roles for Efficient Team Formation by Parameter Learning

  • Dai Hamada
  • Toshiharu Sugawara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7327)

Abstract

We propose a learning method for efficient team formation 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 effective team formation to do tasks has 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 which other agents should be selected as team members and which team it should join. Our experiments demonstrated that the numbers of tasks executed by successfully generated teams increased by approximately 17% compared with a conventional method.

Keywords

Team Member Multiagent System Coalition Formation Random Method Idle State 
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 Eng.Waseda UniversityTokyoJapan

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