Version 1
: Received: 30 September 2024 / Approved: 30 September 2024 / Online: 1 October 2024 (03:37:40 CEST)
How to cite:
Li, X.; Xia, Y.; Gursesli, M. C.; You, X.; Chen, S.; Thawonmas, R. Enhancing Player Experience in an FPS with Dynamic Audio Cue Adjustment Based on Gaussian Progress Regression. Preprints2024, 2024092460. https://doi.org/10.20944/preprints202409.2460.v1
Li, X.; Xia, Y.; Gursesli, M. C.; You, X.; Chen, S.; Thawonmas, R. Enhancing Player Experience in an FPS with Dynamic Audio Cue Adjustment Based on Gaussian Progress Regression. Preprints 2024, 2024092460. https://doi.org/10.20944/preprints202409.2460.v1
Li, X.; Xia, Y.; Gursesli, M. C.; You, X.; Chen, S.; Thawonmas, R. Enhancing Player Experience in an FPS with Dynamic Audio Cue Adjustment Based on Gaussian Progress Regression. Preprints2024, 2024092460. https://doi.org/10.20944/preprints202409.2460.v1
APA Style
Li, X., Xia, Y., Gursesli, M. C., You, X., Chen, S., & Thawonmas, R. (2024). Enhancing Player Experience in an FPS with Dynamic Audio Cue Adjustment Based on Gaussian Progress Regression. Preprints. https://doi.org/10.20944/preprints202409.2460.v1
Chicago/Turabian Style
Li, X., Siyuan Chen and Ruck Thawonmas. 2024 "Enhancing Player Experience in an FPS with Dynamic Audio Cue Adjustment Based on Gaussian Progress Regression" Preprints. https://doi.org/10.20944/preprints202409.2460.v1
Abstract
This paper analyzes the experience of first-person shooter (FPS) players when the game difficulty is adjusted by personalizing their audio cue settings, considering the balance between player performance, modeled using Gaussian Process Regression (GPR), and prior data serving as designer preference. In addition, we investigate the reason why player experience changes according to in-game audio cues. Previous studies have proposed various dynamic difficulty adjustment (DDA) methods for FPS games. However, few studies have considered the role of audio cues in the player experience. This paper compares the player experience of personalized enemy audio cue volume setting (GPR-DDA) and that of predetermined setting in an FPS game. Two comprehensive experimental phases, involving 80 participants, are conducted to assess the efficacy of GPR-DDA. The experience of our players is measured using questions taken from the Game User Experience Satisfaction Scale (GUESS) questionnaire and a final survey asking their open-ended feedback. A large language model (LLM) is used to analyze the natural language expressions of the players according to their native languages. To effectively have an LLM assist a limited number of qualified human evaluators in the classification of player responses, we develop an original procedure for this task. The GUESS results show that GPR-DDA can improve the player experience, and, in addition, the high consistency in the classification results over multiple runs of the selected LLM, as well as the similarity between its results and those of our human evaluators, reflect the reliability of the proposed LLM-assisted procedure.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
