ConvChain

As others have mentioned, this is "just" constraint propagation. I think much of the success is that it's a simple idea to grasp and implement, that provides very good results, and has a compelling name. I don't want to be dismissive, I think it's very cool. The original author of "wave function collapse" also has "convchain" [0] which is similarly "just" using Monte Carlo Markov Chains to sample the space. It's also a kind of "staple" algorithm for physicists, theoretical computer scientists, etc but I guess hasn't caught on as much as WFC. Note that WFC has major problems when the tile set is too constrained. There's a SO question about it [1] and I also happen to have written a small article about it as well [2](). [0] https://github.com/mxgmn/ConvChain [1] https://stackoverflow.com/questions/72721299/why-can-the-wav... [2] https://www

Most of the examples in the repo have those NxN all one color patches. Or, without (C2) the algorithm would have generated completely empty integrated circuits, or completely grass terrain, which is really boring. You understood right, it's constraints + probabilities. Btw, I have different algorithm that satisfies (C2) perfectly, but not (C1): https://github.com/mxgmn/ConvChain

Thanks! No, not really. ConvChain though is related to symmetry breaking, the same way as MCMC simulation of the Ising model is https://github.com/mxgmn/ConvChain