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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Keepaway Soccer: From Machine Learning Testbed to Benchmark
verfasst von : Peter Stone, Gregory Kuhlmann, Matthew E. Taylor, Yaxin Liu
Erschienen in: RoboCup 2005: Robot Soccer World Cup IX
Verlag: Springer Berlin Heidelberg
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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.