Tracery bots were a fun, simple, way of making generative texts. They are basically an easy way to specify generative grammars via a simple JSON file format. There used to be a horde of fun little tracery bots on twitter until API changes shut them all down.
Nowadays, you can prompt a chatbot to get whatever you want. But that lacks the same charm, and it doesn’t give you the control you’d want for something unleashed on the internet. Let’s do something about that.
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Recently I’ve been playing around more with gen AI techniques. I thought I’d try to generate a set of tiles that all connect together. It’s harder than it sounds – Stable Diffusion is hard to control, so there’s no easy way to get a set of images that are fully consistent with one another.
I’ve developed a technique for doing it that I’ll call Non-Manifold Diffusion as it involves doing diffusion over a set of patches that interlock to form a non-manifold surface.
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I went to some lectures on the future of science in games recently, and the keynote speaker was Tommy Thompson, an well-known AI expert in the game dev space.
Of course, by AI, he didn’t mean the modern sort that dominates the news. His focus is AI for games, which is algorithmic and rarely involves any ML component. Still, he spoke about the challenges the industry faces regarding Image Generators, LLMs and so on. He specifically called LLMs “stochastic parrots”, which I found disappointing. Imho it’s an incredibly misleading model of what LLMs are capable of and is usually deployed to downplay their abilities and belittle them. But it’s a common view, particularly in creative industries.
So what is a better model? It’s clear that they are not that smart in most ways we consider important, but they do have some interesting capabilities. Here’s model I use that I feel give a better intuition for what they can and cannot do.
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I’ve been working on adding aperiodic grids to Sylves.
Aperiodic tilings are made tilings are made of a fixed set of tiles, rotated and translated to fully cover the plane.But they are not periodic – there’s no way to rotate/translate the whole grid onto itself.
This makes them almost hypnotic in their balance of regularity and chaos. A classic example is the penrose tiling.
One of my biggest gripes with the WaveFunctionCollapse procedural generation algorithm is that, despite the name, it doesn’t really have anything to do with quantum mechanics. I usually prefer the term Constraint Based Procedural Generation instead.
The name WaveFunctionCollapse is meant more as an analogy. As the algorithm progresses, it resolves a fuzzy, uncertain picture of the output into sharper detail, much as in quantum mechanics, the state of a system is also a range of possibilities, which resolves to something specific when “observed”.
But could we adapt WFC to the Quantum way of thinking, and ran it on actual Quantum Hardware? Well, that’s exactly what is discussed in this new paper Quantum WaveFunctionCollapse by Raoul Heese1 (Youtube summary). Does it work? Is it fast? Let’s find out.
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Rune’s recent talk on layered procedural generation has got me thinking about procedural generation again, so I wanted to share a technique I found about doing a uniform distribution of points on an infinite plane. I assumed this would be a well known thing, but I couldn’t find any references elsewhere.
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Procedural Generation can be interpreted quite broadly as just “making computers make cool creative things”. People make art, games, music, audio, stories and all sorts of weird things.
I’ve been doing it as a hobbyist for some time, and have become more and more involved: I make tutorials, projects, I sell a tool online for a niche algorithm, and recently taught a “masterclass” at Everything Procedural, the main conference for professionals in the space.
I thought I’d spill some digital ink about what it’s actually about. I get asked often enough, and this will help me clarify my verbal answers.
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This is a experiment I tried out for ProcJam 2023. I wasn’t getting great results from it and got bored after a few days, but I’ve decided to share what I did manage.
The rules of the hackathon were changed this year, ruling out most forms of AI. I was thinking – what’s the furthest I can push that rule without crossing the line?
In the end, I designed a system where we used AI to design and build a standalone classical generator.
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Watabou’s Cave Generator is one in a series of RPG-ready map generators that Watabou has created over the years. All his work oozes style, but the cave generator was always the one I found most mysterious.
I discovered that the entire thing was exported with extremely readable javascript, so naturally I started to poke and prod. Let’s go over how it works.
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Last time, we looked at quarter-tiles. This was an auto-tiling technique for square grids. Each cell in the grid is associated with a terrain (i.e. either solid or empty). Then the squares were split in four, and each quarter was assigned an appropriate quarter-tile.
Otho-tiles extends this procedure to work with irregular grids, even non-square grids. We just have to alter the procedure a little, and be ready to deform the quarter tiles fit in place.
Ortho is a Conway Operator. It can be thought of as the extension of dividing a square into 4. It divides each n-gon into n “kites” or “ortho-cells”. Each kite is a four sided shape containing the cell center, one corner, and the midpoint of the two edges adjacent to that corner.
The appeal of the ortho operation is it can take any polygonal grid, no matter how irregular, and convert it into a grid of 4 sided shapes. And it’s much easier to work with something that has a consistent number of sides.
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