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.

翻译：程序化内容生成（PCG）是一个快速发展的领域，在电子游戏行业具有广泛应用前景，能够以远低于人工制作成本的方式帮助创造更优质的游戏。然而，当前PCG研究多聚焦于简单游戏中相对直观的关卡生成，因为复杂场景下难以设计可优化的目标函数。这限制了PCG在更复杂的现代游戏作品中的应用，阻碍了其在工业界的普及。本研究通过引入组合式关卡生成方法来解决这一局限，该方法通过递归组合简单的底层生成器来构建大规模复杂结构。这种途径既保证了目标函数的易优化性，又能通过引用低级组件以可解释的方式设计复杂结构。实验表明，在多个任务中，我们的方法相比非组合式基线能更精确地满足设计师的功能需求。最后，我们通过Minecraft中的定性展示，验证了使用简单基础生成器也能生成规模庞大、结构复杂且保持连贯性的建筑体。