Enhancing Player Experience in a First-Person Shooter with Dynamic Audio Cue Adjustment Based on Gaussian Progress Regression - Xiaoxu Li; Yi Xia; Mustafa Can Gursesli; Xiao You; Siyuan Chen; Ruck Thawonmas (Applied Sciences)
This paper analyzes the experience of first-person shooter (FPS) players when 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 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 settings (GPR-DDA) with that of predetermined settings 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 for 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 ensure the LLM effectively assists a limited number of qualified human evaluators in classifying player responses, we have developed an original procedure for this task. The GUESS results show that GPR-DDA can improve the player experience. 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, reflects the reliability of the proposed LLM-assisted procedure.