Advances in AI-assisted Game Testing

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School of Science | Doctoral thesis (article-based) | Defence date: 2023-03-07
Degree programme
82 + app. 58
Aalto University publication series DOCTORAL THESES, 79/2022
Game testing is an essential part of game development, in which developers try to select a game design that delivers a desirable experience for the players and engages them. However, the interactive nature of games makes the player experience and behavior unpredictable. Therefore, game testing requires collecting a large amount of playtest data in iterative sessions, which makes game testing time and money consuming. Game testing includes a wide range of aspects from finding bugs and balancing game parameters to modeling player behavior and experience. This dissertation mostly concentrates on the player experience aspect. It proposes methods for (partially) automating and facilitating the game testing process. The first part of the dissertation focuses on player emotion analysis and proposes tools and methods for automatically processing and summarizing human playtesters' data. The second part of the dissertation concentrates on simulation-based approaches for modeling player experience and behavior to reduce the need for human playtesters. In the first publication, we use deep neural networks for analyzing player facial expression data and provide a visualization tool for inspecting affect changes at game events, which replicates earlier results of physiological emotion analysis. Next, we extend this work by introducing a new dataset of game streamers' emotions in different granularities and considering other input signals like audio and speech for automatic emotion recognition. In the second part of the dissertation, simulation-based methods and reinforcement learning agents are used to predict game difficulty and engagement and capture the relation between these two game metrics. In summary, this dissertation proposes and evaluates methods that advance automatic game testing by proposing approaches for automatic analysis of player emotions, which can be used for selecting specific segments of playtest videos for further inspection. In addition, we have provided accurate models of player experience and behavior using simulation-based methods that can be used to detect problematic game levels before releasing them to the actual players.
Supervising professor
Hämäläinen, Perttu, Prof., Aalto University, Department of Computer Science, Finland
game testing, player experience, emotion recognition, AI agents, reinforcement learning
Other note
  • [Publication 1]: Roohi, Shaghayegh and Takatalo, Jari and Kivikangas, J. Matias and Hämäläinen, Perttu. Neural Network Based Facial Expression Analysis of Game Events: a Cautionary Tale. In Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play, Melbourne, VIC, Australia, pp. 429–437, 10 2018.
    DOI: 10.1145/3242671.3242701 View at publisher
  • [Publication 2]: Roohi, Shaghayegh and Mekler, Elisa D. and Tavast, Mikke and Blomqvist, Tatu and Hämäläinen, Perttu. Recognizing Emotional Expression in Game Streams. In Proceedings of the Annual Symposium on Computer-Human Interaction in Play, Barcelona, Spain, pp. 301–311, 10 2019.
    DOI: 10.1145/3311350.3347197 View at publisher
  • [Publication 3]: Roohi, Shaghayegh and Relas, Asko and Takatalo, Jari and Heiskanen, Henri and Hämäläinen, Perttu. Predicting Game Difficulty and Churn Without Players. In Proceedings of the Annual Symposium on Computer-Human Interaction in Play, Virtual Event, Canada, pp. 585–593, 11 2020.
    DOI: 10.1145/3410404.3414235 View at publisher
  • [Publication 4]: Roohi, Shaghayegh and Guckelsberger, Christian and Relas, Asko and Takatalo, Jari and Heiskanen, Henri and Hämäläinen, Perttu. Predicting Game Difficulty and Engagement Using AI Players. Proceedings of the ACM on Human-Computer Interaction, vol. 5, no. CHI PLAY, pp. 1-17, 9 2021.
    DOI: 10.1145/3474658 View at publisher