In this paper, we propose a method and workflow for automating regression testing of certain video game aspects using automated planning and incremental action model learning techniques. The basic idea is to use detailed game logs and incremental action model learning techniques to maintain a formal model in the planning domain description language (PDDL) of the gameplay mechanics. 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：本文提出了一种方法和工作流程，利用自动化规划与增量式动作模型学习技术，对某些视频游戏特性进行回归测试的自动化。其基本思想是使用详细的游戏日志以及增量式动作模型学习技术，以规划领域描述语言（PDDL）维护游戏机制的形式化模型。该工作流程使得不具备PDDL或其他形式化系统经验的游戏开发者，与具备PDDL建模经验但缺乏游戏开发技能的人员之间能够高效协作。我们首先对方法和工作流程进行总体描述，随后通过一个具体概念验证示例加以展示——该示例基于流行游戏开发引擎Unity提供的教程项目中的简单角色扮演游戏。本文标志着朝着最小化甚至消除工作流程中对建模专家依赖的第一步，从而让更广泛的受众能够使用自动化规划技术。