WaveFunctionCollapse (WFC) is a procedural generation technique for creating images and tile-based levels. I’ve discussed it many times before.
As a technique, it has some pros and cons. Pro: it’s almost uncannilly good at stitching together tilesets into interesting arrangements, and is pretty good at copying the style in a supplied sample image. Cons: it becomes bland and repetitive at large scales.
In my software Tessera, I’ve been working on various ways of customizating the generation to work around that con. But I’ve seen another way that turns WFC on its head. Instead of using WFC as a full level generator, we want to decide the overall structure of a level some other way, and then use WFC just for the details.
It’s rare that you see a game that gives top billing in its marketing to the quality of its procedurally generated levels. Normally PCG is sprinkled in a game to add a bit of variety, or to make up for the lack of actual level design. But, for 2017’s Unexplored, the rest of the game is there to justify the stellar levels.
Unexplored presents itself as a fairly standard roguelite – enter a randomly generated dungeon, descend 20 levels and retrive the amulet of Yendor. The gameplay features a realtime combat based around timing and aiming your swings, but otherwise plays things by the book.
But it doesn’t take long realize why they much such a big deal out of the procedural generation. Unexplored level design takes more after 2D Zelda games than it does Rogue. Instead of just wandering at random, you quickly find that the path forward is blocked, forcing you to solve puzzles, find items and keys, defeat enemies to continue. There’s a huge variety of structure, all randomly generated, but nearly every level is a tightly packed, interesting space.
Last time, I took inspiration from a game called Unexplored, and wrote about about a system of rule evaluation called Graph Rewriting.
In developing Unexplored and earlier games and academic papers, developer Joris Dormans has over the years developed an entire software library centered around graph rewriting. It’s called PhantomGrammar, and it comes with an accompanying UI called Ludoscope (sadly, neither is publically available currently).
I think it’s worth discussing how it works, as it turns the previous theoretical ideas into something pratical to work with.
I’ve spent a lot of time deconstructing Unexplored, a 2017 indie game by Joris Dormans. It just nails procedurally generated zelda-like dungeons, and I had to know for myself how the magic happens. Fortunately, most of the generation logic is written in a custom language, PhantomGrammar, so between that and some help from the developers, I think I’ve got a pretty good idea how it works.
The Binding of Isaac, and its remake, Binding Of Isaac: Rebirth are one of my favourite games of all time. It’s a roguelite twin stick shooter, much like Enter the Gungeon.
It’s time for another in my series on how games do level generation. Let’s take a look at SLIGE, a random level generator for Doom. The original Doom. That’s right, we’re going back to the early 90s for this one.
Doom was one of the first games designed from the ground up to friendly to modding, and consequently the community around it exploded. In the years following its release, level packs and tools started to circulate for free. It was only a matter of time until someone designed a random level generator.
SLIGE was one of the first. It quickly became infamous because newcomers would often attempt to pass off the level it creates as their own. But they’d inevitably get caught – SLIGE levels have a very distinctive feel, as you can see in the video below.
SLIGE may not be the most sophisticated level generator out there, but its fame caught my eye. It was under development by author David Chess for a number of years, and so has lots to explore. In this article, we’ll delve into how exactly it works.
Since developing DeBroglie and Tessera, I’ve had a lot of requests to explain what it is, how it works. The generation can often seem quite magical, but actually the rules underlying it are quite simple.
So, what is the Wave Function Collapse algorithm (WFC)? Well, it’s an algorithm developed by Maxim Gumin for generating tile based images based off simple configuration or sample images. If you’ve come here hoping to learn about quantum physics, you are going to be disappointed.
WFC is explained briefly in Maxim’s README, but I felt it needed a fuller explanation from first principals. It is a slight twist on a much more broad concept – constraint programming. So much of this article is going to explain constraint programming, and we’ll get back to WFC at the end.
WFC is a very flexible algorithm, particularly with the enhancements I’ve designed, but at the same time, I’ve found it’s quite hard to actually get it to produce practical levels useful for computer games. The key difficulty is WFC doesn’t have any global structure to it, all it does it make the output generation look like the input locally, i.e. when viewing small rectangles of output at a time.
In this article, I share what I’ve learned to take your constraint based generators to the next level.