用进化算法修复生成的破损游戏关卡，让不完整关卡变得可玩。
Evolutionary Level Repair
- 通过进化与质量多样性算法搜索修复方案
- 在少量修改下成功修复机器学习生成的破损关卡
- 适合需要自动生成且可玩性强的游戏内容开发
我们研究游戏关卡修复问题，即对设计但不可用的关卡进行修正，使其具备完整性、物体可达性等性能特征。修复过程需限制修改数量。本文采用基于搜索的进化与质量多样性算法，取得了良好效果。该方法应用于机器学习驱动的程序化内容生成（PCGML）所生成的风格匹配但常出错的关卡，展现了作为混合程序化内容生成（PCG）方法的巨大潜力。
原文摘要 · Abstract (English)
We address the problem of game level repair, which consists of taking a designed but non-functional game level and making it functional. This might consist of ensuring the completeness of the level, reachability of objects, or other performance characteristics. The repair problem may also be constrained in that it can only make a small number of changes to the level. We investigate search-based solutions to the level repair problem, particularly using evolutionary and quality-diversity algorithms, with good results. This level repair method is applied to levels generated using a machine learning-based procedural content generation (PCGML) method that generates stylistically appropriate but frequently broken levels. This combination of PCGML for generation and search-based methods for repair shows great promise as a hybrid procedural content generation (PCG) method.
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