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http://dx.doi.org/10.15207/JKCS.2020.11.10.045

Opponent Move Prediction of a Real-time Strategy Game Using a Multi-label Classification Based on Machine Learning  

Shin, Seung-Soo (Dept. Computer Software, Kwangwoon University)
Cho, Dong-Hee (Dept. Computer Science, Kwangwoon University)
Kim, Yong-Hyuk (Dept. Computer Software, Kwangwoon University)
Publication Information
Journal of the Korea Convergence Society / v.11, no.10, 2020 , pp. 45-51 More about this Journal
Abstract
Recently, many games provide data related to the users' game play, and there have been a few studies that predict opponent move by combining machine learning methods. This study predicts opponent move using match data of a real-time strategy game named ClashRoyale and a multi-label classification based on machine learning. In the initial experiment, binary card properties, binary card coordinates, and normalized time information are input, and card type and card coordinates are predicted using random forest and multi-layer perceptron. Subsequently, experiments were conducted sequentially using the next three data preprocessing methods. First, some property information of the input data were transformed. Next, input data were converted to nested form considering the consecutive card input system. Finally, input data were predicted by dividing into the early and the latter according to the normalized time information. As a result, the best preprocessing step was shown about 2.6% improvement in card type and about 1.8% improvement in card coordinates when nested data divided into the early.
Keywords
Machine learning; Real-time strategy game; Multi-label classification; Data preprocessing; Prediction;
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