On Automating Video Game Testing by Planning and Learning

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.