分析游戏关卡对微小输入变化的敏感性，揭示其独特数据特性。
Analysis of Robustness of a Large Game Corpus
- 定义数据鲁棒性，衡量关卡对输入微小变化的敏感程度。
- 构建涵盖4款经典游戏的大规模关卡数据集，解决PCGML数据稀疏问题。
- 对比主流机器学习数据集，揭示游戏关卡在结构与约束上的独特性。
基于机器学习的游戏程序化内容生成（PCGML）利用机器学习技术创建地图和关卡等游戏内容。2D瓦片类游戏关卡因其简化形式但仍保留游戏典型约束（如可解性）而成为PCGML的标准数据集。本文揭示了游戏关卡的独特属性：结构化的离散数据特性、局部与全局约束的存在，以及对输入微小变化的高度敏感性。我们定义数据鲁棒性为衡量输入微小变化导致输出改变的程度，并以此分析和比较这些关卡与当前先进机器学习数据集的差异，凸显其内在特性的微妙不同。此外，我们从四款受经典瓦片游戏启发的游戏中构建了一个大规模关卡数据集，显著扩充了现有数据量，缓解了PCGML中的数据稀疏挑战。
原文摘要 · Abstract (English)
Procedural content generation via machine learning (PCGML) in games involves using machine learning techniques to create game content such as maps and levels. 2D tile-based game levels have consistently served as a standard dataset for PCGML because they are a simplified version of game levels while maintaining the specific constraints typical of games, such as being solvable. In this work, we highlight the unique characteristics of game levels, including their structured discrete data nature, the local and global constraints inherent in the games, and the sensitivity of the game levels to small changes in input. We define the robustness of data as a measure of sensitivity to small changes in input that cause a change in output, and we use this measure to analyze and compare these levels to state-of-the-art machine learning datasets, showcasing the subtle differences in their nature. We also constructed a large dataset from four games inspired by popular classic tile-based games that showcase these characteristics and address the challenge of sparse data in PCGML by providing a significantly larger dataset than those currently available.
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