In games, as in many other domains, design validation and testing is a significant challenge as systems are growing in size and manual testing is becoming infeasible. In this position paper we outline an approach to automated game validation based on an imitation learning technique, and provide an analysis of the potential benefits to automated game testing. The method leverages a data-driven 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 evaluate the validity of our claim by conducting a user study with industry experts. The survey results presented in this paper demonstrate the potential of a data-driven approach to reduce effort and enhance the quality of game testing. Moreover, the survey reveals several open challenges. To this end, we analyze the identified challenges and provide a basis for further research and discussion, as well as to help guide the development of imitation learning for game testing.

Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning / Sestini, Alessandro; Bergdahl, Joakim; Tollmar, Konrad; Bagdanov, Andrew D.; Gisslén, Linus. - ELETTRONICO. - (2023), pp. 1-8. (Intervento presentato al convegno IEEE Conference on Games) [10.1109/cog57401.2023.10333234].

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

Sestini, Alessandro;Bagdanov, Andrew D.;
2023

Abstract

In games, as in many other domains, design validation and testing is a significant challenge as systems are growing in size and manual testing is becoming infeasible. In this position paper we outline an approach to automated game validation based on an imitation learning technique, and provide an analysis of the potential benefits to automated game testing. The method leverages a data-driven 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 evaluate the validity of our claim by conducting a user study with industry experts. The survey results presented in this paper demonstrate the potential of a data-driven approach to reduce effort and enhance the quality of game testing. Moreover, the survey reveals several open challenges. To this end, we analyze the identified challenges and provide a basis for further research and discussion, as well as to help guide the development of imitation learning for game testing.
2023
Proceedings of the 2023 IEEE Conference on Games
IEEE Conference on Games
Sestini, Alessandro; Bergdahl, Joakim; Tollmar, Konrad; Bagdanov, Andrew D.; Gisslén, Linus
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1369392
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