分层组合关卡生成器以创建复杂结构
Hierarchically Composing Level Generators for the Creation of Complex Structures
AI总结：
针对程序化内容生成难以应用于复杂游戏关卡的问题，提出递归组合简单底层生成器的组合式关卡生成方法，在多任务中优于非组合基线，可生成大型连贯复杂结构。
摘要：
程序化内容生成（PCG）是一个不断发展的领域，在电子游戏行业有众多应用，且极具潜力以手工创作成本的一小部分助力打造更优质的游戏。然而，PCG领域的大量研究都聚焦于在简单游戏中生成相对直接的关卡，因为为复杂场景设计可优化的目标函数颇具挑战。这限制了PCG在更复杂的现代游戏中的适用性，阻碍了其在行业中的应用。我们的研究旨在解决这一局限，提出一种组合式关卡生成方法，该方法递归组合简单的底层生成器以构建大型复杂的创作成果。这种方法可实现易于优化的目标，并且能够通过参考底层组件，以可解释的方式设计复杂结构。我们通过实验证明，我们的方法在多项任务中能更准确地满足设计者的功能需求，表现优于非组合式基线方法。最后，我们提供了一个（在Minecraft中的）定性展示示例，说明了使用简单基础生成器生成的大型复杂但仍具连贯性的结构。
英文摘要：
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.
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