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.
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.