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@InProceedings{software-challenge97,
        Author="Hiroaki Kitano and Milind Tambe and Peter Stone and  Manuela Veloso and Silvia Coradeschi and Eiichi Osawa and Hitoshi Matsubara and Itsuki Noda and Minoru Asada", 
        Title ="The {R}obo{C}up Synthetic Agent Challenge 97",
        booktitle =  IJCAI97,
        Publisher="Morgan Kaufmann",
        Address="San Francisco, CA",
        pages="24--29",
        Year="1997",
        abstract={
                  RoboCup Challenge offers a set of challenges for
                  intelligent agent researchers using a friendly
                  competition in a dynamic, real-time, multi-agent
                  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 evalute their techniques, but
                  also challenges researchers to evolve these
                  techniques to face key constraints fundamental to
                  this domain: real-time, uncertainty, and teamwork.
        },
        wwwnote={<a href="http://ijcai.org/">IJCAI-97</a><br>
                 <a href="http://www.cs.utexas.edu/~pstone/Papers/97synthetic-challenge/synthetic-challenge.html">HTML version</a>.},
}