Summary of Improving Conditional Level Generation Using Automated Validation in Match-3 Games, by Monica Villanueva Aylagas et al.
Improving Conditional Level Generation using Automated Validation in Match-3 Games
by Monica Villanueva Aylagas, Joakim Bergdahl, Jonas Gillberg, Alessandro Sestini, Theodor Tolstoy, Linus Gisslén
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 presents Avalon, a novel generative model for level design in game production. The proposed method uses a conditional variational autoencoder to generate match-3 levels, conditioned on pre-collected statistics such as game mechanics like difficulty and visual features like size and symmetry. This approach addresses the limitations of existing models by providing users with control over the generation process and validating generated levels for solvability. The paper evaluates Avalon through quantitative comparisons with an ablated model and qualitative analysis to ensure preservation of the dataset style. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make video game levels using machine learning. Right now, computers can generate levels, but they often don’t make sense or are too easy/hard to play. The problem is that these levels aren’t always fun or challenging for players. To fix this, researchers developed a new method called Avalon, which uses statistics about how people play games to create better levels. This approach helps ensure that the generated levels are enjoyable and solvable. By analyzing the results, we can see that Avalon makes more valid levels than previous methods. |
Keywords
» Artificial intelligence » Generative model » Machine learning » Variational autoencoder
