Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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Opponent Move Prediction of a Real-time Strategy Game Using a Multi-label Classification Based on Machine Learning

기계학습 기반 다중 레이블 분류를 이용한 실시간 전략 게임에서의 상대 행동 예측

  • 신승수 (광운대학교 소프트웨어학부) ;
  • 조동희 (광운대학교 컴퓨터과학과) ;
  • 김용혁 (광운대학교 소프트웨어학부)
  • Received : 2020.09.11
  • Accepted : 2020.10.20
  • Published : 2020.10.28
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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.

최근 많은 게임이 사용자의 게임 플레이와 관련된 데이터를 제공하고 있고, 이에 기계학습 기법을 결합하여 상대의 행동을 예측하는 연구들이 있다. 본 연구는 실시간 전략 게임(클래시로얄)의 경기 데이터와 기계학습 기반의 다중 레이블 분류를 사용하여 상대 플레이어의 행동을 예측한다. 초기 실험은 이진 형태의 카드 특성과 카드 배치 좌표 그리고 정규화된 시간 정보를 입력받아 카드 타입, 카드 배치 좌표를 랜덤포레스트와 다층 퍼셉트론을 이용하여 예측한다. 이후, 순차적으로 3 가지 전처리 방식을 사용하여 실험을 진행했다. 먼저 입력 데이터의 특성 정보 일부를 변환시켜 예측했다. 다음으로 입력 데이터를 연속된 카드 입력 방식까지 고려한 중첩 형태로 변환 시켜 예측했다. 마지막으로 모든 이전 단계의 데이터들을 정규화된 시간 기준에 따라 초반, 후반으로 분할하여 예측했다. 그 결과 가장 개선을 보인 전처리 방식은 중첩 형태의 데이터를 초반으로 분할하였을 경우로 카드 타입이 약 2.6%, 카드 배치 좌표가 약 1.8% 개선을 보였다.

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References

  1. K. Arulkumaran, A. Cully & J. Togelius. (2019). Alphastar: an evolutionary computation perspective. Proceedings of the Genetic and Evolutionary Computation Conference Companion. (pp. 314-315). Prague : ACM. DOI : 10.1145/3319619.3321894
  2. A. Dockhorn, M. Frick, U. Akkaya & R. Kruse. (2018, May). Predicting opponent moves for improving hearthstone AI. Information Processing and Management of Uncertainty in Knowledge-based Systems. Theory and Foundations (pp. 621-632). Cadiz : Springer. DOI : 10.1007/978-3-319-91476-3_51
  3. I. S. Baek et al. (2020). Real-time strategy game win-lose prediction model using 3d convolutional neural networks. Journal of The Korean Institute of Industrial Engineers, 46(4), 349-355. DOI : 10.7232/JKIIE.2020.46.4.349
  4. D. Silver et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529, 484-489. DOI : 10.1038/nature16961
  5. C. Olston & M. Najork. (2010). Web crawling. Foundations and Trends in Information Retrieval, 4(3), 175-246. https://doi.org/10.1561/1500000017
  6. T. Grigorios & L. Katakis. (2007). Multi-label classification: an overview. International Journal of Data Warehousing and Mining, 3(3), 1-13. https://doi.org/10.4018/jdwm.2007070101
  7. S. L. Enrique, C. Bielza, E. F. Morales, P. Hernandez-Leal, J. H. Zaragoz & P. Larranaga. (2014). Multi-label classification with bayesian network-based chain classifiers. Pattern Recognition Letters, 41, 14-22. https://doi.org/10.1016/j.patrec.2013.11.007
  8. L. Andy & M. Wiener. (2002). Classification and regression by randomforest. R News, 2(3), 18-22.
  9. M. Riedmiller. (1994). Advanced supervised learning in multi-layer perceptrons-from backpropagation to adaptive learning algorithms. Computer Standards & Interfaces, 16(3), 265-278. https://doi.org/10.1016/0920-5489(94)90017-5
  10. S. R. Safavian & D. Landgrebe. (1991). A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics. 21(3), 660-674. https://doi.org/10.1109/21.97458
  11. L. Ceriani & P. Verme. (2012). The origins of the gini index: extracts from variabilita e mutabilita (1912) by corrado gini. The Journal of Economic Inequality, 10(3), 421-443. DOI :10.1007/s10888-011-9188-x
  12. T. Hastie, R. Tibshirani & J. Friedman. (2009). The elements of statistical learning: data mining, inference, and prediction. New york : Springer.
  13. V. Nair & G. E. Hinton. (2010). Rectified linear units improve restricted Boltzmann machines. International Conference on Machine Learning. (pp. 807-814). Haifa : Omnipress.
  14. D. P. Kingma & J. Ba. (2014). Adam: a method for stochastic optimization. 3rd International Conference for Learning Repesentation.. Sandiego : ICLR.
  15. F. Akinori, I. Hideki & J. Suzuki. (2008). Multi-label text categorization with model combination based on f1-score maximization. Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II. (pp 823-828).
  16. Y. W. Nam & Y. H. Kim. (2018). Prediction of drifter trajectory using evolutionary computation. Discrete Dynamics in Nature and Society 2018. DOI : 10.1155/2018/6848745