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.

本文主要介绍了一种基于组合生成的方法，在复杂和现代游戏中实现可优化性，解决算法设计复杂度高的问题，并在游戏设计中优化功能要求，旨在推动程序内容生成技术在视频游戏产业中的应用。

逐层组合级别生成器以创建复杂结构

Hierarchically Composing Level Generators for the Creation of Complex Structures

A rapidly growing body of research on compositional generalization
investigates the ability of a semantic parser to dynamically recombine
linguistic elements seen in training into unseen sequences. We present a
systematic comparison of sequence-to-sequence models and models guided by
compositional principles on the recent COGS corpus (Kim and Linzen, 2020).
Though seq2seq models can perform well on lexical tasks, they perform with
near-zero accuracy on structural generalization tasks that require novel
syntactic structures; this holds true even when they are trained to predict
syntax instead of semantics. In contrast, compositional models achieve
near-perfect accuracy on structural generalization; we present new results
confirming this from the AM parser (Groschwitz et al., 2021). Our findings show
structural generalization is a key measure of compositional generalization and
requires models that are aware of complex structure.

该研究系统比较了序列到序列模型和基于组合原则的模型在 COGS 语料库上处理组合泛化能力的能力，在结构泛化任务中，基于序列的模型表现接近零，而组成模型在结构泛化任务中表现接近完美，这表明结构泛化是组成泛化的关键度量，需要能够处理复杂结构的模型。