Peter Stone's Selected Publications

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Keepaway Soccer: From Machine Learning Testbed to Benchmark

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.
Some simulations of keepaway referenced in the paper and keepaway software.
Official version from Publisher's Webpage© Springer-Verlag

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Abstract

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.

BibTeX Entry

@incollection(LNAI2005-keepaway,
        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.
        },
        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>&copy Springer-Verlag},
)

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