Classification of Tilesets

Many years ago I started looking at different sorts of tiles sets used by artists. A good tile set is flexible enough to allow tiles to be re-used in a lot of situation, but simple enough that the tiles can be easily created. Ideally, it would enable autotiling or otherwise be easy to design levels with.

Though I covered a few different techniques back then, I fell short of any systematic discussion of tiles. Here I plan to take a more rigorous approach, in the hopes of making a common language for referring to different tile sets, and pointing out the key variations in design. Maybe we’ll even discover something new, like Mendelev predicting new elements for the periodic table.

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Infinite Modifying in Blocks

I’m going to share with you a technique I’ve found for doing lazy, reliable, deterministic, constant-time infinite generation of tile based levels using Wave Function Collapse (WFC). But first, let’s cover some background, Modifying in Blocks, and lazy chunk based infinite generation.

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Constraint-Based Tile Generators

Last article, we were comparing WaveFunctionCollapse (WFC), and Model Synthesis (MS). These are both similar procedural generation techniques that work along similar lines. Specifically, they generate a grid of tiles (or pixels), using a set of hard constraints, and some generalized solver technique to find a solution, a set of tiles that satisfies all the constraints provided. Let’s call this overall class of thing constraint-based tile generators.

The techniques have been pretty popular in recent years, and part of that is because it’s actually a very easy concept to experiment with and extend. Many of the more practical examples in the wild include their own extensions, as basic WFC tends to produce levels that look a bit repetitive and structureless.

Today I thought I’d do a review of all the different ways that you can customize this generation technique, showing how WFC and MS are subsets of a greater class. The whole family of such algorithms is much larger and I think there’s a lot of potential still to explore.

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Beyond Basic Autotiling

Autotiling is a system for automatically picking which tile to place on the map, based on user input. It’s a fast way to design levels without having to fiddle with every tile indivually to ensure they are all consistent. Autotiling becoming increasingly well supported by game engines.

An exceptionally fancy autotiler, Tilekit

While there’s not a great deal of standardization at the moment, I’d say that 90% of systems at the moment are based on marching squares, which chooses between 16 tiles based on user input at each corner, or blob, which chooses between 47 tiles based on user input on each tile. I’ve written about these and other schemes before.

These approaches do work quite well for quickly whipping up a level, after the initial slog of creating the tiles, but when you start getting into more complex cases, a major shortcoming appears: combinatorial explosion.

Basically, as autotilers chose one tile for each situation, there needs to be a tile ready for any given combination. It’s bad enough creating 47 tiles needed for the blob pattern, but that only handles transitions between two different terrains. If you add a third, you need hundreds of tiles. And even simpler patterns quickly blow up with a few terrains.

Today, we look at a few ways to deal with that.

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Driven WaveFunctionCollapse

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.

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Dungeon Generation in Unexplored

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.

https://cdn.akamai.steamstatic.com/steam/apps/506870/ss_af6440b995f113d35999af111e0e0e80aa77c53b.600x338.jpg?t=1574761931

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.

Full video here
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PhantomGrammar and Ludoscope

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.

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Graph Rewriting for Procedural Level Generation

I’ve been doing this series on how games do level generation for some time, and I have a complete beauty for you.

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 ideas of Unexplored are so interesting that I felt they deserved an article in it’s own right. The game is centered around a concept called Graph Rewriting, which, while well understood academically, is rarely used in games. I’m going to spend this article talking about that technique alone, then how PhantomGrammar specifically uses and extends it. Finally I will talk about how these techniques are put together in Unexplored to make such sophisticated levels.

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