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Comparing Two Representations for Evolving Micro in 3D RTS Games

机译:比较3D RTS游戏中演变为Micro的两种表示形式

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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:我们对在三维实时策略游戏的小规模场景中使用遗传算法生成成功的操纵行为(或微观行为)感兴趣。在先前的工作中,我们将参数化的3D微观行为(例如目标选择和装备)编码为用于控制战斗中友军的算法。然后,遗传算法调整了这些参数,以指导部队机动,以赢得小规模冲突。在这项研究中,我们研究了仅使用影响图和13个潜在场的组合的微观行为的新表示形式。然后,遗传算法调整影响图和潜在场参数,以演化出获胜的微观行为。在称为FastEcslent的完整3D RTS游戏环境中,我们将两种表示形式在相同场景下与相同对手的性能进行比较。结果表明,使用我们使用较少领域知识的新表示形式的遗传算法能够可靠地演化出高质量的3D微行为,该行为略微但显着优于我们先前的工作。因此,我们的工作为利用潜在领域为三维RTS游戏生成高质量,复杂,微观的可行性提供了证据。

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