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.

翻译：由机器学习模型生成的程序化关卡若不经过进一步编辑可能无法通关。已有多种方法通过在后期处理步骤中强制执行硬约束来自动修复这些关卡。然而，随着关卡规模增大，这些基于约束的修复方法会变得愈发缓慢。本文提出利用可解释性方法识别导致关卡无法通关的具体区域。通过为这些区域分配更高权重，基于约束的求解器能够优先处理这些有问题的区域，从而实现更高效的修复。我们在三款游戏中的测试结果表明，该方法能够帮助更快地修复程序化生成的关卡。