UvA Trilearn 2005 team description

Jelle R. Kok and Nikos Vlassis. UvA Trilearn 2005 team description. In Proceedings CD RoboCup 2005, Springer-Verlag, Osaka, Japan, July 2005.
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Abstract

This paper shortly describes the main features of the UvA Trilearn soccer simulation team, which participates in the RoboCup competition since 2001. In the last years we mostly concentrated on the coordination of the different agents. For this we applied the framework of coordination graphs. In this framework, a high-level strategy is specified using value rules which describe the effectiveness of a (possible joint) action in a specific situation. During a game, a variable elimination algorithm is applied on all applicable rules in order to find the individual actions for the agents which maximize the global effectiveness. Variable elimination is exact, but it does not scale well to systems where many agents depend on each other. In our UvA Trilearn 2005 team, we therefore experiment with the usage of the max-plus algorithm, as an approximate alternative to variable elimination.

BibTeX Entry

@InProceedings{Kok05robocupTeam,
  author =       {Jelle R. Kok and Nikos Vlassis},
  title =        {{U}v{A} {T}rilearn 2005 team description},
  booktitle =    {Proceedings CD RoboCup 2005},
  year =         {2005},
  month =        jul,
  address =      {Osaka, Japan},
  editor =       {I. Noda and A. Jacoff and A. Bredenfeld and
                  Y. Takahashi},
  publisher =    {Springer-Verlag},
  wwwnote =      {<a href=
                  "http://www.springer.de/comp/lncs/index.html">
                  Publisher's Webpage</a>&copy Springer-Verlag},
  postscript =   {2005/Kok05robocupTeam.ps.gz},
  pdf =          {2005/Kok05robocupTeam.pdf},
  abstract =     { This paper shortly describes the main features of
                  the UvA Trilearn soccer simulation team, which
                  participates in the RoboCup competition since
                  2001. In the last years we mostly concentrated on
                  the coordination of the different agents. For this
                  we applied the framework of coordination graphs. In
                  this framework, a high-level strategy is specified
                  using value rules which describe the effectiveness
                  of a (possible joint) action in a specific
                  situation. During a game, a variable elimination
                  algorithm is applied on all applicable rules in
                  order to find the individual actions for the agents
                  which maximize the global effectiveness. Variable
                  elimination is exact, but it does not scale well to
                  systems where many agents depend on each other. In
                  our UvA Trilearn 2005 team, we therefore experiment
                  with the usage of the max-plus algorithm, as an
                  approximate alternative to variable elimination. }
}

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