Abstract
This study presents an in-game audio-cue setting approach using Gaussian Process Regression (GPR) to provide dynamic difficulty adjustment (DDA) in a first-person shooter game. A novel difficulty-recommendation method is proposed to set our GPR goal for a better personalized audio cue setting. Recent studies showed that audio cues could give the player significant affective changes and influence the player’s performance. In addition, other studies showed that it is effective to train the player’s in-game ability by adjusting audio cues. However, no research has used audio cues as the primary factor in determining game difficulty. Experimental results show that our game can reach a recommended difficulty within a few plays.
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Notes
- 1.
Demo videos: https://tinyurl.com/FPSaudioCues.
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Acknowledgment
JingYi Gao from Tokyo Polytechnic University contributed to this work by providing game modifications. Ratchanon Nobnop from our laboratory contributed to this work with technical support.
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Li, X., Wira, M., Thawonmas, R. (2022). Toward Dynamic Difficulty Adjustment with Audio Cues by Gaussian Process Regression in a First-Person Shooter. In: Göbl, B., van der Spek, E., Baalsrud Hauge, J., McCall, R. (eds) Entertainment Computing – ICEC 2022. ICEC 2022. Lecture Notes in Computer Science, vol 13477. Springer, Cham. https://doi.org/10.1007/978-3-031-20212-4_12
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