将波函数坍缩框架化为马尔可夫决策过程，提升复杂场景生成效率。
A Markovian Framing of WaveFunctionCollapse for Procedurally Generating Aesthetically Complex Environments
- 把WFC重构成马尔可夫决策过程，分离约束与目标优化
- 在多领域测试中，新方法优于传统联合优化策略
- 适合需要高美学复杂度的程序化环境生成任务
程序化内容生成常需同时满足设计目标和由基础瓦片集隐含的邻接约束。为解决两者联合优化的挑战，我们把波函数坍缩（WFC）重构为马尔可夫决策过程（MDP），使外部优化算法可仅聚焦于目标最大化，而利用WFC的传播机制自动满足约束。我们在多个不同难度领域进行实证比较，发现联合优化在任务复杂度上升时表现恶化，且始终劣于对WFC-MDP的优化，凸显了将局部约束满足与全局目标优化解耦的优势。
原文摘要 · Abstract (English)
Procedural content generation often requires satisfying both designer-specified objectives and adjacency constraints implicitly imposed by the underlying tile set. To address the challenges of jointly optimizing both constraints and objectives, we reformulate WaveFunctionCollapse (WFC) as a Markov Decision Process (MDP), enabling external optimization algorithms to focus exclusively on objective maximization while leveraging WFC's propagation mechanism to enforce constraint satisfaction. We empirically compare optimizing this MDP to traditional evolutionary approaches that jointly optimize global metrics and local tile placement. Across multiple domains with various difficulties, we find that joint optimization not only struggles as task complexity increases, but consistently underperforms relative to optimization over the WFC-MDP, underscoring the advantages of decoupling local constraint satisfaction from global objective optimization.
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