Keepaway Soccer: From Machine Learning Testbed to Benchmark (2006)
Keepaway soccer has been previously put forth as a emphtestbed for machine learning. Although multiple researchers have used it successfully for machine learning experiments, doing so has required a good deal of domain expertise. This paper introduces a set of programs, tools, and resources designed to make the domain easily usable for experimentation without any prior knowledge of RoboCup or the Soccer Server. In addition, we report on new experiments in the Keepaway domain, along with performance results designed to be directly comparable with future experimental results. Combined, the new infrastructure and our concrete demonstration of its use in comparative experiments elevate the domain to a machine learning emphbenchmark, suitable for use by researchers across the field.
In RoboCup-2005: Robot Soccer World Cup IX, Itsuki Noda and Adam Jacoff and Ansgar Bredenfeld and Yasutake Takahashi (Eds.), Vol. 4020, pp. 93-105, Berlin 2006. Springer Verlag.

Gregory Kuhlmann Ph.D. Alumni kuhlmann [at] cs utexas edu
Yaxin Liu Postdoctoral Alumni
Peter Stone Faculty pstone [at] cs utexas edu
Matthew Taylor Ph.D. Alumni taylorm [at] eecs wsu edu