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

Challenges can be seen as a type of game that motivates participants to solve
serious tasks. As a result, competition organizers must develop effective game
rules. However, these rules have multiple objectives beyond making the game
enjoyable for participants. These objectives may include solving real-world
problems, advancing scientific or technical areas, making scientific
discoveries, and educating the public. In many ways, creating a challenge is
similar to launching a product. It requires the same level of excitement and
rigorous testing, and the goal is to attract ''customers'' in the form of
participants. The process begins with a solid plan, such as a competition
proposal that will eventually be submitted to an international conference and
subjected to peer review. Although peer review does not guarantee quality, it
does force organizers to consider the impact of their challenge, identify
potential oversights, and generally improve its quality. This chapter provides
guidelines for creating a strong plan for a challenge. The material draws on
the preparation guidelines from organizations such as Kaggle 1 , ChaLearn 2 and
Tailor 3 , as well as the NeurIPS proposal template, which some of the authors
contributed to.

挑战可以被看作是一种激励参与者解决严肃任务的游戏，竞争组织者必须制定有效的游戏规则，并且这些规则有多个目标，除了使游戏对参与者有趣之外，还可能包括解决现实问题、推进科学或技术领域、进行科学发现和教育公众。本文提供了为挑战创建强大计划的指南。