Summary of You-only-randomize-once: Shaping Statistical Properties in Constraint-based Pcg, by Jediah Katz et al.
You-Only-Randomize-Once: Shaping Statistical Properties in Constraint-based PCG
by Jediah Katz, Bahar Bateni, Adam M. Smith
First submitted to arxiv on: 1 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Logic in Computer Science (cs.LO)
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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 proposes a novel method for procedural content generation, specifically addressing the challenge of modeling both local and global constraints on generated output. By treating the generation task as a constraint satisfaction problem, the authors define hard constraints that control the output’s overall quality. However, this approach does not account for statistical properties, such as the distribution of design elements in reference designs. To address this limitation, the paper introduces You-Only-Randomize-Once (YORO) pre-rolling, a technique that orders decision variables to encode desired statistics in a constraint-based generator. The authors demonstrate the effectiveness of YORO pre-rolling using WaveFunctionCollapse (WFC) as an example, showcasing its ability to control tile-grid output statistics while enforcing global constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers create new content, like levels or art designs, by giving it rules to follow. The challenge is that these rules should balance local and global goals. Right now, there’s no easy way to make sure the generated content looks good and meets certain statistical requirements. For example, we might want a game level to have similar elements and patterns as another level in the same game. This paper proposes a new method called YORO pre-rolling that helps solve this problem by telling the computer what statistics are important and how to prioritize them. |
