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2017 | OriginalPaper | Buchkapitel

Classifying the Strategies of an Opponent Team Based on a Sequence of Actions in the RoboCup SSL

verfasst von : Yusuke Adachi, Masahide Ito, Tadashi Naruse

Erschienen in: RoboCup 2016: Robot World Cup XX

Verlag: Springer International Publishing

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Abstract

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.

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Vorheriges Kapitel Distributed Averages of Gradients (DAG): A Fast Alternative for Histogram of Oriented Gradients

Nächstes Kapitel A 3D Face Modelling Approach for Pose-Invariant Face Recognition in a Human-Robot Environment

We used \(TH_p = 400\,\text {mm}\) and \(n=3\) in our experiments.

In the equation, the threshold is denoted by \(TH_s\), and we used \(TH_s=400\,\text {mm}\) in our experiments.

In the equation, the threshold is denoted by \(TH_b\), and we used \(TH_b=800\,\text {mm}\) in our experiments. Both \(\alpha \) and \(\beta \) in Eq. (2) were set to the value 0.5.

This series of actions is defined over each time frame.

For the parameter h, we ran the program over the range \(0.03-0.07\) and found that \(h=0.06\) gave the best results.

The number of clusters was not known in advance in this experiment, so the k-means method could not be used. Ward’s method and the group average clustering apply under circumstances of an unknown number of clusters. In our experiment, these approaches gave similar clustering results. The computational cost of the group average clustering was lower than the cost associated with Ward’s method; therefore, we used the group average clustering.

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Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 1(2), 224–227 (1979)CrossRef

Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. (Am. Stat. Assoc.) 66(336), 846–850 (1971)CrossRef

Trevizan, F.W., Veloso, M.M.: Learning opponent’s strategies in the RoboCup small size league. In: Proceedings of AAMAS 2010 Workshop on Agents in Real-Time and Dynamic Environments (2010)

Visser, U., Weland, H.-G.: Using online learning to analyze the opponent’s behavior. In: Kaminka, G.A., Lima, P.U., Rojas, R. (eds.) RoboCup 2002. LNCS, vol. 2752, pp. 78–93. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45135-8_7CrossRef

Yasui, K., et al.: A new detection method of kick actions from logged data of SSL games. JSAI Technical report SIG-Challenge-B201-6 (2012). (in Japanese)

Yasui, K., Kobayashi, K., Murakami, K., Naruse, T.: Analyzing and learning an opponent’s strategies in the RoboCup small size league. In: Behnke, S., Veloso, M., Visser, A., Xiong, R. (eds.) RoboCup 2013. LNCS, vol. 8371, pp. 159–170. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44468-9_15CrossRef

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Yasui, K., Ito, M., Naruse, T.: Classifying an opponent’s behaviors for real-time learning in the RoboCup small size league. IEICE Trans. Info. Syst. J97–D(8), 1297–1306 (2014). (in Japanese)

Titel: Classifying the Strategies of an Opponent Team Based on a Sequence of Actions in the RoboCup SSL
verfasst von: Yusuke Adachi
Masahide Ito
Tadashi Naruse
Verlag: Springer International Publishing
Buch: RoboCup 2016: Robot World Cup XX
Print ISBN: 978-3-319-68791-9

Electronic ISBN: 978-3-319-68792-6

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-68792-6_9

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