论文标题
星际争霸1和2中的地形分析作为组合优化
Terrain Analysis in StarCraft 1 and 2 as Combinatorial Optimization
论文作者
论文摘要
实时策略游戏中的地形分析是允许空间推理的必要步骤。地形分析的目的是收集和处理有关地图拓扑和属性的数据，以具有定性的空间表示。在《星际争霸》游戏中，所有以前的地形分析作品都提出了基于连接的组件检测，Voronoi图计算和修剪以及区域合并的清晰分析。这些方法已作为特定于游戏的库实现，它们只能为所有地图和所有用户提供相同类型的分析。在本文中，我们提出了一种将地形分析视为组合优化问题的方法。我们的方法允许通过更改问题模型中的约束或目标函数来进行不同的分析。我们还提出了一个图书馆，嘲讽，实施我们的方法，并能够处理Starcraft 1和Starcraft 2地图。这使我们的图书馆成为具有不同空间表示需求的星际争霸机器人的通用工具。我们认为，我们的图书馆可以解锁具有真正的自适应AIS扮演星际争霸的可能性，并且可以成为机器人新浪潮的起点。
Terrain analysis in Real-Time Strategy games is a necessary step to allow spacial reasoning. The goal of terrain analysis is to gather and process data about the map topology and properties to have a qualitative spatial representation. On StarCraft games, all previous works on terrain analysis propose a crisp analysis based on connected component detection, Voronoi diagram computation and pruning, and region merging. Those methods have been implemented as game-specific libraries, and they can only offer the same kind of analysis for all maps and all users. In this paper, we propose a way to consider terrain analysis as a combinatorial optimization problem. Our method allows different kinds of analysis by changing constraints or the objective function in the problem model. We also present a library, Taunt, implementing our method and able to handle both StarCraft 1 and StarCraft 2 maps. This makes our library a universal tool for StarCraft bots with different spatial representation needs. We believe our library unlocks the possibility to have real adaptive AIs playing StarCraft, and can be the starting point of a new wave of bots.
