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In games, as in and many other domains, design validation and testing is ahuge challenge as systems are growing in size and manual testing is becominginfeasible. This paper proposes a new approach to automated game validation andtesting. Our method leverages a data-driven imitation learning technique, whichrequires little effort and time and no knowledge of machine learning orprogramming, that designers can use to efficiently train game testing agents.We investigate the validity of our approach through a user study with industryexperts. The survey results show that our method is indeed a valid approach togame validation and that data-driven programming would be a useful aid toreducing effort and increasing quality of modern playtesting. The survey alsohighlights several open challenges. With the help of the most recentliterature, we analyze the identified challenges and propose future researchdirections suitable for supporting and maximizing the utility of our approach.

Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning