Summary of Guided Game Level Repair Via Explainable Ai, by Mahsa Bazzaz and Seth Cooper
Guided Game Level Repair via Explainable AI
by Mahsa Bazzaz, Seth Cooper
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the challenge of repairing procedurally generated levels in video games using machine learning models. Existing methods rely on enforcing hard constraints during post-processing, but these become increasingly slow as level size increases. The proposed solution leverages explainability methods to identify unsolvable regions and prioritize repairs through constraint-based solvers. By doing so, this approach enables faster repair times for larger levels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, video game designers create levels using special computer programs called machine learning models. Sometimes, these levels become “unsolvable” without further editing. To fix this problem, researchers developed ways to automatically edit the levels by following strict rules. However, as levels get bigger, these repair methods become slower and less efficient. This paper suggests a new approach that helps identify which parts of the level are causing the problems and prioritizes fixing those areas first. By doing so, it can fix larger levels faster. |
Keywords
* Artificial intelligence * Machine learning
