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Algorithms that derive output structures from input examples using probabilistic constraint satisfaction.
Distinguishing note: Distinct from general procedural generation by its reliance on probabilistic inference from example data.
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WaveFunctionCollapse is a procedural generation engine that creates complex, non-repeating patterns by treating spatial arrangement as a constraint satisfaction problem. It functions as a stochastic solver that derives output structures from a single input example, ensuring that every element placed within a grid satisfies specific adjacency requirements relative to its neighbors. The system distinguishes itself by using an entropy-driven approach to grid collapse, where it iteratively selects the cell with the fewest remaining possibilities to trigger a cascade of logical updates. By decompo
A probabilistic algorithm that derives output structures from input examples by treating generation as a constraint satisfaction problem with local dependencies.