This research introduces Procedural Artificial Narrative using Generative AI (PANGeA), a structured approach for leveraging large language models (LLMs), guided by a game designer's high-level criteria, to generate narrative content for turn-based role-playing video games (RPGs). Distinct from prior applications of LLMs used for video game design, PANGeA innovates by not only generating game level data (which includes, but is not limited to, setting, key items, and non-playable characters (NPCs)), but by also fostering dynamic, free-form interactions between the player and the environment that align with the procedural game narrative. The NPCs generated by PANGeA are personality-biased and express traits from the Big 5 Personality Model in their generated responses. PANGeA addresses challenges behind ingesting free-form text input, which can prompt LLM responses beyond the scope of the game narrative. A novel validation system that uses the LLM's intelligence evaluates text input and aligns generated responses with the unfolding narrative. Making these interactions possible, PANGeA is supported by a server that hosts a custom memory system that supplies context for augmenting generated responses thus aligning them with the procedural narrative. For its broad application, the server has a REST interface enabling any game engine to integrate directly with PANGeA, as well as an LLM interface adaptable with local or private LLMs. PANGeA's ability to foster dynamic narrative generation by aligning responses with the procedural narrative is demonstrated through an empirical study and ablation test of two versions of a demo game. These are, a custom, browser-based GPT and a Unity demo. As the results show, PANGeA holds potential to assist game designers in using LLMs to generate narrative-consistent content even when provided varied and unpredictable, free-form text input.

翻译：本研究提出了基于生成式人工智能的程序化人工叙事（PANGeA），这是一种结构化方法，利用大型语言模型（LLM），在游戏设计师高层级准则的指导下，为回合制角色扮演视频游戏（RPG）生成叙事内容。与以往将LLM用于视频游戏设计的应用不同，PANGeA的创新之处在于不仅生成游戏关卡数据（包括但不限于场景、关键物品和非玩家角色），还促进玩家与环境之间符合程序化游戏叙事的动态、自由形式的交互。PANGeA生成的NPC具有人格偏向性，并在其生成的回应中体现大五人格模型的特性。PANGeA解决了处理自由形式文本输入所带来的挑战，这类输入可能引发超出游戏叙事范围的LLM回应。该系统采用一种新颖的验证机制，利用LLM的智能评估文本输入，并使生成的回应与展开的叙事保持一致。为实现这些交互，PANGeA由一个服务器支持，该服务器托管一个定制记忆系统，为增强生成的回应提供上下文，从而使其与程序化叙事保持一致。为支持广泛应用，该服务器提供REST接口，允许任何游戏引擎直接与PANGeA集成，同时配备可适配本地或私有LLM的接口。通过一项实证研究以及对演示游戏两个版本（即定制的基于浏览器的GPT演示和Unity演示）的消融测试，证明了PANGeA通过使回应与程序化叙事保持一致来促进动态叙事生成的能力。结果表明，即使在提供多样且不可预测的自由形式文本输入时，PANGeA仍具备协助游戏设计师利用LLM生成叙事一致性内容的潜力。