On Automating Video Game Regression Testing by Planning and Learning
This work addresses the problem of reducing the need for modeling experts in game testing workflows, making automated planning more accessible to game developers, though it is incremental as it builds on existing planning and learning techniques.
The authors tackled automating regression testing in video games by using automated planning and incremental action model learning to maintain a formal model of gameplay mechanics, demonstrating it on a simple Unity-based role-playing game as a proof-of-concept.
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.