Guided Game Level Repair via Explainable AI
This addresses efficiency issues in game level repair for developers, but it is incremental as it builds on existing constraint-based methods.
The paper tackles the problem of slow repair times for unsolvable procedurally generated game levels by using explainability methods to identify problematic regions, enabling faster repairs across three games.
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.