Hierarchically Composing Level Generators for the Creation of Complex Structures

Abstract

Procedural content generation (PCG) is a growing field, with numerous applications in the video game industry and great potential to help create better games at a fraction of the cost of manual creation. However, much of the work in PCG is focused on generating relatively straightforward levels in simple games, as it is challenging to design an optimisable objective function for complex settings. This limits the applicability of PCG to more complex and modern titles, hindering its adoption in industry. Our work aims to address this limitation by introducing a compositional level generation method that recursively composes simple low-level generators to construct large and complex creations. This approach allows for easily-optimisable objectives and the ability to design a complex structure in an interpretable way by referencing lower-level components. We empirically demonstrate that our method outperforms a non-compositional baseline by more accurately satisfying a designer’s functional requirements in several tasks. Finally, we provide a qualitative showcase (in Minecraft) illustrating the large and complex, but still coherent, structures that were generated using simple base generators.

Publication
IEEE Transactions on Games
Michael Beukman
Michael Beukman

I like doing cool things, such as generating levels in Minecraft, teaching robots how to kick a ball and I do rock climbing in my spare time.

Muhammad Umair Nasir
Muhammad Umair Nasir

I love tackling challenges in Open-ended Learning and Jiu Jitsu.

Branden Ingram
Branden Ingram
Lecturer

I am primarily interested in AI for games.

Benjamin Rosman
Benjamin Rosman
Lab Director

I am a Professor in the School of Computer Science and Applied Mathematics at the University of the Witwatersrand in Johannesburg. I work in robotics, artificial intelligence, decision theory and machine learning.

Steven James
Steven James
Deputy Lab Director

My research interests include reinforcement learning and planning.