In this paper, we propose a method and workflow for automating the testing of certain video game aspects using automated planning and planning action model learning techniques. The basic idea is to generate detailed gameplay logs and apply action model learning to obtain a formal model in the planning domain description language (PDDL). The workflow enables efficient cooperation of game developers without any experience with PDDL or other formal systems and a person experienced with PDDL modeling but no game development skills. We describe the method and workflow in general and then demonstrate it on a concrete proof-of-concept example -- a simple role-playing game provided as one of the tutorial projects in the popular game development engine Unity. This paper presents the first step towards minimizing or even eliminating the need for a modeling expert in the workflow, thus making automated planning accessible to a broader audience.

翻译：本文提出了一种利用自动规划与规划动作模型学习技术实现特定视频游戏方面自动化测试的方法与工作流。其核心思想是通过生成详细游戏日志并应用动作模型学习，获取以规划领域描述语言（PDDL）表示的形式化模型。该工作流能使缺乏PDDL或其他形式系统经验的游戏开发者，与具备PDDL建模知识但无游戏开发经验的人员高效协作。本文先阐述了该方法与工作流的通用框架，随后以流行游戏引擎Unity教程项目中的简易角色扮演游戏为例，通过具体概念验证实例进行演示。本研究标志着向最小化甚至消除工作流中建模专家依赖迈出第一步，从而让自动规划技术能为更广泛受众所用。