In this paper, we propose a new method for classifying the strategies of an opponent in the RoboCup Soccer Small-Size League. Each strategy generates a sequence of basic actions selected from a kick action, a mark action, or other similar actions. Here, we identify strategies by classifying an observed sequences of basic actions selected by an opponent during a game. This method greatly improves our previous method [9] in the following two ways: the previous method was applicable mainly to set plays, whereas this restriction is lifted in our new method. Additionally, our new method requires a lower computational time than the previous method. Assuming that our team was the opponent team, our team’s strategies were evaluated using the Rand Index, yielding a value exceeding 0.877 in 3 out of 4 games. A Rand index value exceeding 0.840 was obtained from an analysis of the 4 opponent teams (1 game for each opponent team). These Rand indices represent a high level of classification algorithm performance.
CITATION STYLE
Adachi, Y., Ito, M., & Naruse, T. (2017). Classifying the strategies of an opponent team based on a sequence of actions in the RoboCup SSL. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9776 LNAI, pp. 109–120). Springer Verlag. https://doi.org/10.1007/978-3-319-68792-6_9
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