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Procedural Content Generation - The Open Source Success Story of Wave Function Collapse

2023; Association for Computing Machinery; Volume: 15; Issue: 2 Linguagem: Inglês

10.1145/3708973.3708979

1947-4598

Mathias Lux,

Semantic Web and Ontologies

With OpenAI's Dall-E, Midjourney, and Adobe Firefly, computer-generated visual content has hit the mass market. Machine learning-based algorithms can now create, and re-mix multimedia content based on huge corpora of images and videos and relieve creative professionals of tedious work. While this has gained much momentum lately, procedurally generated content (PCG) has been around for quite some time already. Especially in video game development, randomized levels, behavior, aesthetics, and even narratives increase replayability and engage the audience longer. Prominent examples are Minecraft and Diablo, where the game world is randomly generated, and Borderlands, where in-game items are generated on the fly. PCG is applied on multiple levels with different purposes, like generating terrain, weather, road and transport networks, house layouts, puzzles, textures, and mazes, just to name a few. Hedrikx et al. [1] give a comprehensive overview of the topic. In a typical scenario, a mix of algorithms is employed to create content on the fly. Generative grammar algorithms are often employed for vegetation, fractal noise is used to generate terrain and clouds, and simulation is used to create road networks or to erode terrain further. Lately, deep learning-based approaches have become available. The most notable example is AI Dungeon [2], where users converse with GPT in a text adventure. However, large neural networks require significant computational power and are hard to explain and constrain, so procedural content generation tends to use less complex algorithms, where game designers can give hard constraints to influence the outcome.