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Peter Stone, Gregory Kuhlmann, Matthew
E. Taylor, and Yaxin Liu. Keepaway Soccer: From Machine Learning Testbed to Benchmark. In Itsuki Noda, Adam
Jacoff, Ansgar Bredenfeld, and Yasutake Takahashi, editors, RoboCup-2005: Robot Soccer World Cup IX, pp. 93–105,
Springer-Verlag, Berlin, 2006. 28% acceptance rate at RoboCup-2005
Some simulations
of keepaway referenced in the paper and keepaway software.
Official version from Publisher's
Webpage© Springer-Verlag
Keepaway soccer has been previously put forth as a testbed 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 benchmark, suitable for use by researchers across the field.
@incollection(ROBOCUP05-stone,
author="Peter Stone and Gregory Kuhlmann and Matthew E.\ Taylor and Yaxin Liu",
title="Keepaway Soccer: From Machine Learning Testbed to Benchmark",
booktitle= "{R}obo{C}up-2005: Robot Soccer World Cup {IX}",
Editor="Itsuki Noda and Adam Jacoff and Ansgar Bredenfeld and Yasutake Takahashi",
Publisher="Springer-Verlag",address="Berlin",year="2006",
volume="4020",
pages="93--105",
abstract={
Keepaway soccer has been previously put forth as a
\emph{testbed} 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
\emph{benchmark}, suitable for use by researchers
across the field.
},
note="28% acceptance rate at {R}obo{C}up-2005",
wwwnote={Some <a href="http://www.cs.utexas.edu/users/AustinVilla/sim/keepaway/">simulations of keepaway</a> referenced in the paper and keepaway software.<br>Official version from <a href="http://dx.doi.org/10.1007/11780519_9">Publisher's Webpage</a>© Springer-Verlag},
)
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