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Comparing Two Representations for Evolving Micro in 3D RTS Games

机译:比较三个在3D RTS游戏中不断发展的微观的表示

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We are interested in using genetic algorithms to generate winning maneuvering behaviors (or micro) in skirmish scenarios for three dimensional Real-Time Strategy games. In prior work, we encoded parameterized 3D micro behaviors like target selection and kiting into an algorithm for controlling friendly units in battle. Genetic algorithms then tuned these parameters to guide unit maneuvering in order to win skirmishes. In this study, we investigate a new representation for micro behaviors that uses only an influence map and a combination of thirteen potential fields. Genetic algorithms then tune influence map and potential field parameters to evolve winning micro behaviors. We compare the performance of both representations on identical scenarios against identical opponents in a full 3D RTS game environment called FastEcslent. The results show that the genetic algorithm using our new representation using less domain knowledge, reliably evolved high quality 3D micro behaviors that slightly, but significantly, outperformed behaviors from our prior work. Our work thus provides evidence for the viability of using potential fields for generating high quality, complex, micro for three dimensional RTS games.
机译:我们有兴趣使用遗传算法在三维实时战略游戏中生成赢取机动行为(或微型)。在事先工作中,我们编码了目标选择和塑料等参数化的3D微行为,以控制友好单位的算法。遗传算法然后调整这些参数以指导单元操纵以赢得小冲突。在这项研究中,我们调查了仅使用影响图的微观行为和十三个潜在领域的组合的新表现形式。遗传算法然后调整影响地图和潜在场参数,以发展获胜的微观行为。我们在一个名为FeateCslent的完整3D RTS游戏环境中对与相同对手的相同方案的表现进行比较。结果表明,遗传算法使用我们的新代表使用较少的领域知识,可靠地演化的高质量3D微观行为,略微,而且显着,从我们的事先工作中表现出优于优势。因此,我们的工作提供了使用潜在领域的可行性来产生高质量,复杂,微型三维RTS游戏的证据。

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