• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.112-115
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• 2018

PvP 게임에서의 밸런스는 재미있는 게임을 만들고자 하는 많은 개발사들의 지속적인 관심사였다. 또한 게임의 성공과 안정적인 운영을 위해서 밸런스가 중요하다는 인식이 생겨나면서 그 중요성은 날로 높아지고 있다. 본 논문에서는 글로벌 버전 출시 직후 131개국에서 매출 1위를 달성하고, 양대 마켓에서 '올해의 게임'으로 꼽히며 선풍적인 인기를 얻었던 클래시 로얄을 통해 PvP 게임 밸런스 방법에 대해서 살펴보고 안정적인 게임 밸런스를 위한 방안을 제안한다.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.45-51
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• 2020

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.