Co-Evolving Influence Map Tree Based Strategy Game Players

C. Miles, J. Quiroz, Ryan E. Leigh, S. Louis
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引用次数: 46

Abstract

We investigate the use of genetic algorithms to evolve AI players for real-time strategy games. To overcome the knowledge acquisition bottleneck found in using traditional expert systems, scripts, or decision trees we evolve players through co-evolution. Our game players are implemented as resource allocation systems. Influence map trees are used to analyze the game-state and determine promising places to attack, defend, etc. These spatial objectives are chained to non-spatial objectives (train units, build buildings, gather resources) in a dependency graph. Players are encoded within the individuals of a genetic algorithm and co-evolved against each other, with results showing the production of strategies that are innovative, robust, and capable of defeating a suite of hand-coded opponents
共同进化影响基于地图树的策略游戏玩家
我们研究了使用遗传算法来进化实时策略游戏中的AI玩家。为了克服在使用传统专家系统、脚本或决策树时发现的知识获取瓶颈,我们通过共同进化来进化玩家。我们的游戏玩家被执行为资源分配系统。影响地图树用于分析游戏状态,并确定有希望攻击或防御的地点等。在依赖关系图中,这些空间目标与非空间目标(训练单位、建造建筑、收集资源)联系在一起。玩家被编码在一个遗传算法的个体中,相互对抗,共同进化,结果显示策略的产生是创新的,强大的,能够击败一组手工编码的对手
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