Adaptive Online Opponent Game Policy Modeling with Association Rule Mining

Damijan Novak, Iztok Fister
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引用次数: 2

Abstract

Opponent modeling is a research aspect that needs close attention when facing an opponent in a complex game environment. Real-Time Strategy (RTS) games are a representative of one of the highest complex game environments. In RTS games, the players' tactical and strategical decisions need constant adaptation in order to win the game. In this work, a preliminary study is reported on opponent modeling in RTS games via the incorporation of association rule mining to identify game policies in online mode. Such insight into opponents' mode of operation should provide a player with vital information regarding choosing advantageous gameplay countermeasures against an opponent.
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基于关联规则挖掘的自适应在线对手博弈策略建模
对手建模是在复杂游戏环境中面对对手时需要密切关注的一个研究方面。即时战略(RTS)游戏是最复杂的游戏环境之一的代表。在RTS游戏中,为了赢得游戏,玩家需要不断调整战术和战略决策。在这项工作中,通过结合关联规则挖掘来识别在线模式下的游戏策略,对RTS游戏中的对手建模进行了初步研究。这种对对手操作模式的洞察将为玩家提供关于选择对抗对手的有利玩法对策的重要信息。
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