I was browsing the Apache Arrow docs and spotted a term unfamiliar to me. Intrguied, I discovered that Compressed Sparse Fibers are a new technique for representing sparse tensors in memory. After reading up a bit, I thought I’d share with you what I’ve learnt. The technique is so new (well, 2015..) it is not mentioned on Wikipedia, and I found virtually nothing elsewhere. There’s a very limited number of ways to handle sparse data, so it’s always interesting to see a new one.
Don’t worry, I’d also never heard of a sparse tensor before, so I’m going to explain things right from the beginning, assuming you have a basic CS background, and don’t mind me going a little quickly.
I recent entered make a game for PROCJAM 2020. As I was making it purely to fun (there’s no winners to the competition), I focussed thing to make something that expanded my skills and was technically impressive. As such, there’s lots of interesting techniques that I felt were worth briefly explaning here.
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.