用可解释AI定位关卡死锁区域，加速自动修复。
Guided Game Level Repair via Explainable AI
- 通过可解释性方法识别导致关卡无法通关的区域
- 对问题区域加权后修复速度提升显著
- 适用于需要快速生成可玩关卡的开发者
由机器学习生成的程序化关卡可能因设计缺陷而不可解，现有方法在后期处理中施加硬约束进行自动修复，但随着关卡规模增大，修复效率急剧下降。本文提出利用可解释性方法定位导致关卡不可解的具体区域，并对这些区域赋予更高权重，使基于约束的求解器优先处理问题区域，从而实现更高效的修复。在三个不同游戏上的实验表明，该方法能显著加快程序化生成关卡的修复速度。
原文摘要 · Abstract (English)
Procedurally generated levels created by machine learning models can be unsolvable without further editing. Various methods have been developed to automatically repair these levels by enforcing hard constraints during the post-processing step. However, as levels increase in size, these constraint-based repairs become increasingly slow. This paper proposes using explainability methods to identify specific regions of a level that contribute to its unsolvability. By assigning higher weights to these regions, constraint-based solvers can prioritize these problematic areas, enabling more efficient repairs. Our results, tested across three games, demonstrate that this approach can help to repair procedurally generated levels faster.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。