Peter Stone's Selected Publications

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The RoboCup Synthetic Agent Challenge 97

The RoboCup Synthetic Agent Challenge 97.
Hiroaki Kitano, Milind Tambe, Peter Stone, Manuela Veloso, Silvia Coradeschi, Eiichi Osawa, Hitoshi Matsubara, Itsuki Noda, and Minoru Asada.
In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, pp. 24–29, Morgan Kaufmann, San Francisco, CA, 1997.
IJCAI-97
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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, 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.

BibTeX Entry

@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>.},
}

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