• Title/Summary/Keyword: Automated game balance

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Game Elements Balancing using Deep Learning in Artificial Neural Network (딥러닝이 적용된 게임 밸런스에 관한 연구 게임 기획 방법론의 관점으로)

  • Jeon, Joonhyun
    • Journal of the HCI Society of Korea
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    • v.13 no.3
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    • pp.65-73
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    • 2018
  • Game balance settings are crucial to game design. Game balancing must take into account a large amount of numerical values, configuration data, and the relationship between elements. Once released and served, a game - even for a balanced game - often requires calibration according to the game player's preference. To achieve sustainability, game balance needs adjustment while allowing for small changes. In fact, from the producers' standpoint, game balance issue is a critical success factor in game production. Therefore, they often invest much time and capital in game design. However, if such a costly game cannot provide players with an appropriate level of difficulty, the game is more likely to fail. On the contrary, if the game successfully identifies the game players' propensity and performs self-balancing to provide appropriate difficulty levels, this will significantly reduce the likelihood of game failure, while at the same time increasing the lifecycle of the game. Accordingly, if a novel technology for game balancing is developed using artificial intelligence (AI) that offers personalized, intelligent, and customized service to individual game players, it would bring significant changes to the game production system.

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A Study on Game Bot Detection Using Self-Similarity in MMORPGs (자기 유사도를 이용한 MMORPG 게임봇 탐지 시스템)

  • Lee, Eun-Jo;Jo, Won-Jun;Kim, Hyunchul;Um, Hyemin;Lee, Jina;Kwon, Hyuk-min;Kim, Huy-Kang
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.26 no.1
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    • pp.93-107
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    • 2016
  • Game bot playing is one of the main risks in Massively Multi-Online Role Playing Games(MMORPG) because it damages overall game playing environment, especially the balance of the in-game economy. There have been many studies to detect game bot. However, the previous detection models require continuous maintenance efforts to train and learn the game bots' patterns whenever the game contents change. In this work, we have proposed a machine learning technique using the self-similarity property that is an intrinsic attribute in game bots and automated maintenance system. We have tested our method and implemented a system to major three commercial games in South Korea. As a result, our proposed system can detect and classify game bots with high accuracy.