The rapid iteration and frequent updates of modern video games pose significant challenges to the efficiency and specificity of testing. Although automated playtesting methods based on Large Language Models (LLMs) have shown promise, they often lack structured knowledge accumulation mechanisms, making it difficult to conduct precise and efficient testing tailored for incremental game updates. To address this challenge, this paper proposes a KLPEG framework. The framework constructs and maintains a Knowledge Graph (KG) to systematically model game elements, task dependencies, and causal relationships, enabling knowledge accumulation and reuse across versions. Building on this foundation, the framework utilizes LLMs to parse natural language update logs, identify the scope of impact through multi-hop reasoning on the KG, enabling the generation of update-tailored test cases. Experiments in two representative game environments, Overcooked and Minecraft, demonstrate that KLPEG can more accurately locate functionalities affected by updates and complete tests in fewer steps, significantly improving both playtesting effectiveness and efficiency.

翻译：现代电子游戏的快速迭代与频繁更新对测试的效率和针对性提出了重大挑战。尽管基于大语言模型（LLMs）的自动化玩法测试方法已展现出潜力，但它们通常缺乏结构化的知识积累机制，难以针对增量游戏更新进行精准且高效的测试。为应对这一挑战，本文提出了一种KLPEG框架。该框架构建并维护一个知识图谱（KG），以系统化地建模游戏元素、任务依赖关系及因果关系，从而实现跨版本的知识积累与复用。在此基础上，框架利用大语言模型解析自然语言更新日志，通过在知识图谱上进行多跳推理来识别影响范围，从而生成针对更新的测试用例。在《Overcooked》和《Minecraft》两个代表性游戏环境中的实验表明，KLPEG能够更准确地定位受更新影响的功能，并以更少的步骤完成测试，显著提升了玩法测试的效果与效率。