Learning soccer drills for the small size league of RoboCup
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2015-01
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Abstract
This paper shows the results of applying machine learning
techniques to the problem of predicting soccer plays in the Small Size
League of RoboCup. We have modeled the task as a multi-class classification problem by learning the plays of the STOx’s team. For this,
we have created a database of observations for this team’s plays and
obtained key features that describe the game state during a match. We
have shown experimentally, that these features allow two learning classifiers to obtain high prediction accuracies and that most miss-classified
observations are found early on the plays.