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, which recursively composes simple, low-level generators together 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.

翻译：程序化内容生成（PCG）是一个不断发展的领域，在视频游戏行业有着广泛应用，并且具有以远低于手工制作的成本帮助创建更优质游戏方面的巨大潜力。然而，目前PCG的大部分工作都集中在为简单游戏生成相对直接的关卡上，因为要为复杂场景设计可优化的目标函数极具挑战性。这限制了PCG在更复杂、更现代的游戏中的应用，阻碍了其在工业界的推广。我们的工作旨在通过引入一种组合式关卡生成方法来解决这一局限性，该方法递归地将简单的低级生成器组合在一起，以构建大型且复杂的作品。这种方法使得目标函数易于优化，并且能够通过引用底层组件以可解释的方式设计复杂结构。我们通过实验证明，在多个任务中，我们的方法比非组合的基线方法能更精确地满足设计者的功能需求。最后，我们（在《我的世界》中）提供了一个定性展示，展示了使用简单基础生成器所生成的庞大、复杂且仍然连贯的结构。