Recently, the emergence of large language models (LLMs) has unlocked new
opportunities for procedural content generation. However, recent attempts
mainly focus on level generation for specific games with defined game rules
such as Super Mario Bros. and Zelda. This paper investigates the game
generation via LLMs. Based on video game description language, this paper
proposes an LLM-based framework to generate game rules and levels
simultaneously. Experiments demonstrate how the framework works with prompts
considering different combinations of context. Our findings extend the current
applications of LLMs and offer new insights for generating new games in the
area of procedural content generation.

近期，大型语言模型的出现为过程性内容生成带来了新的机遇。这篇论文探讨了通过大型语言模型生成游戏的可能性，提出了一种基于视频游戏描述语言的大型语言模型框架，可以同时生成游戏规则和关卡。实验结果展示了这个框架如何根据不同的上下文进行生成，为过程性内容生成领域中的新游戏生成提供了新的见解。

通过 LLMs 进行游戏生成：视频游戏描述语言的研究

Generating Games via LLMs: An Investigation with Video Game Description  Language

Many advancements have been made in procedural content generation for games,
and with mixed-initiative co-creativity, have the potential for great benefits
to human designers. However, co-creative systems for game generation are
typically limited to specific genres, rules, or games, limiting the creativity
of the designer. We seek to model games abstractly enough to apply to any
genre, focusing on designing game systems and mechanics, and create a
controllable, co-creative agent that can collaborate on these designs. We
present a model of games using state-machine-like components and resource
flows, a set of controllable metrics, a design evaluator simulating
playthroughs with these metrics, and an evolutionary design balancer and
generator. We find this system to be both able to express a wide range of games
and able to be human-controllable for future co-creative applications.

通过模拟状态机组件和资源流、一组可控指标、模拟游戏试玩的设计评估器以及进化式设计平衡器和生成器，我们设计了一个能够表达多样游戏并可供未来共同创作应用的人类可控的协作代理系统。