Optimal strategy selection of non-player character on real time strategy game using a speciated evolutionary algorithm

Su-Hyung Jang, Jongwon Yoon, Sung-Bae Cho
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引用次数: 30

Abstract

In the real-time strategy game, success of AI depends on consecutive and effective decision making on actions by NPCs in the game. In this regard, there have been many researchers to find the optimized choice. This paper confirms the improvement of NPC performance in a real-time strategy game by using the speciated evolutionary algorithm for such decision making on actions, which has been largely applied to the classification problems. Creation and selection of members to use for this ensemble method is manifested through speciation and the performance is verified through ‘conqueror’, a real-time strategy game platform developed by our previous work.
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基于物种进化算法的实时策略博弈非玩家角色最优策略选择
在即时战略游戏中,AI的成功取决于游戏中npc连续有效的行动决策。在这方面,已经有很多研究者在寻找最优的选择。本文通过使用特定的进化算法来进行行动决策,证实了NPC在实时策略游戏中的性能提高,该算法已大量应用于分类问题。创建和选择用于该集成方法的成员通过物种形成来体现,并通过我们之前开发的实时策略游戏平台“征服者”来验证性能。
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