The RoboCup synthetic agent challenge 97

  • Authors:

  • Hiroaki Kitano;Milind Tambe;Peter Stone;Manuela Veloso;Silvia Coradeschi;Eiichi Osawa;Hitoshi Matsubara;Itsuki Noda;Minora Asada

  • Affiliations:

  • Sony Computer Science Laboratory, Shinagawa, Tokyo, Japan;ISI, USC;Carnegie Mellon University;Carnegie Mellon University;Linkoeping University;Sony Computer Science Laboratory;ElectroTechnical Laboratory;ElectroTechnical Laboratory;Osaka University

  • Venue:
  • IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
  • Year:
  • 1997

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Abstract

RoboCup Challenge offers a set of challenges for intelligent agent researchers using a friendly competition in a dynamic, real-time, multiagent domain. While RoboCup in general envisions longer range challenges over the next few decades, RoboCup Challenge presents three specific challenges for the next two years: (i) learning of individual agents and teams; (ii) multi-agent team planning and plan-execution in service of teamwork; and (iii) opponent modeling. RoboCup Challenge provides a novel opportunity for machine learning, planning, and multi-agent researchers it not only supplies a concrete domain to evaluate their techniques, but also challenges researchers to evolve these techniques to face key constraints fundamental to this domain: real-time, uncertainty, and teamwork.