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首页> 外文会议>Entertainment computing - ICEC 2009 >Extending the Strada Framework to Design an AI for ORTS
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Extending the Strada Framework to Design an AI for ORTS

机译：扩展Strada框架为ORTS设计AI

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摘要

Strategy games constitute a significant challenge for game AI, as they involve a large number of states, agents and actions. This makes indeed the decision and learning algorithms difficult to design and implement. Many commercial strategy games use scripts in order to simulate intelligence, combined with knowledge which is in principle not accessible to human players, such as the position of the enemy base or the offensive power of its army. Nevertheless, recent research on adaptive techniques has shown promising results. The goal of this paper is to present the extension such a research methodology, named Strada, so that it is made applicable to the real-time strategy platform ORTS. The adaptations necessary to make Strada applicable to ORTS are detailed and involve the use of dynamic tactical points and specific training scenario for the learning AI. Two sets of experiments are conducted to evaluate the performances of the new method.
机译：策略游戏对游戏AI构成了重大挑战，因为它们涉及大量的状态，代理和动作。这确实使决策和学习算法难以设计和实现。许多商业策略游戏使用脚本来模拟情报，并结合了人类玩家原则上无法获得的知识，例如敌军基地的位置或其军队的进攻能力。尽管如此，最近对自适应技术的研究已显示出令人鼓舞的结果。本文的目的是提出一种扩展的研究方法，称为Strada，以便使其适用于实时战略平台ORTS。详细介绍了使Strada适用于ORTS的必要调整，其中涉及对学习型AI使用动态战术要点和特定的训练场景。进行了两组实验以评估新方法的性能。

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