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.

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In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, 24-29, San Francisco, CA, 1997. Morgan Kaufmann.

@InProceedings{software-challenge97, title={The RoboCup Synthetic Agent Challenge 97}, 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}, booktitle={Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence}, address={San Francisco, CA}, publisher={Morgan Kaufmann}, pages={24-29}, url="http://nn.cs.utexas.edu/?software-challenge97", year={1997} }