Building Placement Optimization in Real-Time Strategy Games
Building Placement Optimization in Real-Time Strategy Games
复制标题
在即时策略游戏中构建布局优化
DOI：
10.1609/aiide.v10i2.12735
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
M. Buro
中科院分区：
文献类型：
--
作者：
Nicolas A. Barriga;Marius Stanescu;M. Buro
在本文中，我们建议使用遗传算法来优化实时策略游戏中建筑物的位置。候选方案通过运行基地攻击模拟来评估。我们在星际争霸战斗模拟器SparCraft中展示了实验结果，使用了从人类和机器人星际争霸游戏中提取的战斗设置。我们展示了我们的系统能够将防御者的损失转化为胜利，并减少幸存攻击者的数量。性能在很大程度上取决于用于训练的攻击者军队组成的预测质量，以及它与用于评估的军队的相似性。这些结果适用于人类和机器人游戏。
In this paper we propose using a Genetic Algorithm to optimize the placement of buildings in Real-Time Strategy games. Candidate solutions are evaluated by running base assault simulations. We present experimental results in SparCraft — a StarCraft combat simulator --- using battle setups extracted from human and bot StarCraft games. We show that our system is able to turn base assaults that are losses for the defenders into wins, as well as reduce the number of surviving attackers. Performance is heavily dependent on the quality of the prediction of the attacker army composition used for training, and its similarity to the army used for evaluation. These results apply to both human and bot games.