TL;DR
This paper introduces YORO, a method for encoding desired statistical properties into constraint-based procedural content generation, enabling better control over output distributions while maintaining global constraints.
Contribution
YORO provides a novel decision variable ordering technique for constraint solvers that encodes statistical properties, improving control in constraint-based generators like WFC.
Findings
YORO effectively controls output statistics in WFC-like generators.
The method maintains enforcement of global constraints.
Applicable to off-the-shelf SAT solvers for diverse generation tasks.
Abstract
In procedural content generation, modeling the generation task as a constraint satisfaction problem lets us define local and global constraints on the generated output. However, a generator's perceived quality often involves statistics rather than just hard constraints. For example, we may desire that generated outputs use design elements with a similar distribution to that of reference designs. However, such statistical properties cannot be expressed directly as a hard constraint on the generation of any one output. In contrast, methods which do not use a general-purpose constraint solver, such as Gumin's implementation of the WaveFunctionCollapse (WFC) algorithm, can control output statistics but have limited constraint propagation ability and cannot express non-local constraints. In this paper, we introduce You-Only-Randomize-Once (YORO) pre-rolling, a method for crafting a decision…
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