Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning
This addresses the problem of infeasible manual testing in growing game systems for game designers, though it appears incremental as it builds on existing imitation learning techniques.
The paper tackles the challenge of automated game validation and testing by proposing an imitation learning method that allows designers to train testing agents with minimal effort, and a user study with industry experts confirms its validity as a useful aid for reducing effort and increasing quality in playtesting.
In games, as in and many other domains, design validation and testing is a huge challenge as systems are growing in size and manual testing is becoming infeasible. This paper proposes a new approach to automated game validation and testing. Our method leverages a data-driven imitation learning technique, which requires little effort and time and no knowledge of machine learning or programming, that designers can use to efficiently train game testing agents. We investigate the validity of our approach through a user study with industry experts. The survey results show that our method is indeed a valid approach to game validation and that data-driven programming would be a useful aid to reducing effort and increasing quality of modern playtesting. The survey also highlights several open challenges. With the help of the most recent literature, we analyze the identified challenges and propose future research directions suitable for supporting and maximizing the utility of our approach.