Game Knowledge Management System: Schema-Governed LLM Pipeline for Executable Narrative Generation in RPGs

Abstract

Procedural approaches have long been used in game development to reduce authoring costs and increase content diversity; however, traditional rule-based systems struggle to scale narrative complexity, whereas recent large language model (LLM)-based methods often produce outputs that are structurally invalid or incompatible with real-time game engines. This gap reflects a fundamental limitation in current practice: generative models lack systematic mechanisms for managing executable game knowledge rather than merely producing free-form narrative texts. To address this issue, we propose a Game Knowledge Management System (G-KMS) that reformulates LLM-based narrative generation as a structured knowledge management process. The proposed framework integrates knowledge grounding, schema-governed generation, normalization-based repair, engine-aligned knowledge admission, and application within a unified pipeline. The system was evaluated on a compact 2D Unity-based RPG benchmark using automated structural and semantic analyses, engine-level playability probes, and a controlled human player study. The experimental results demonstrated high reliability in knowledge admission, stable procedural structures, controlled expressive diversity, and a strong alignment between system-level metrics and player-perceived narrative quality, indicating that LLMs can function as dependable knowledge-construction components when embedded within a governed management pipeline. Beyond the evaluated RPG setting, this study suggests a practical and reproducible approach that may be extended to other executable systems, such as interactive simulations and training environments.