| Issue |
ITM Web Conf.
Volume 73, 2025
International Workshop on Advanced Applications of Deep Learning in Image Processing (IWADI 2024)
|
|
|---|---|---|
| Article Number | 02016 | |
| Number of page(s) | 10 | |
| Section | Machine Learning, Deep Learning, and Applications | |
| DOI | https://doi.org/10.1051/itmconf/20257302016 | |
| Published online | 17 February 2025 | |
A Review of Multi-Armed Bandit Algorithms in Player Modeling and Game Design
School of Computer Science & Technology, Huazhong University of Science and Technology, Wuhan, China
* Corresponding author: u202215549@hust.edu.cn
This paper explores the application of multi-armed bandit algorithms (MAB) in game design, focusing on player modeling and game optimization. The effectiveness of multi-armed bandit algorithms in modeling player characteristics such as skill level, play style, and social comparison orientation is investigated. The potential of MAB in optimizing game design elements like difficulty, rewards, and user interface is also explored. The paper presents empirical results from simulations and user studies and concludes by discussing the potential of MAB algorithms in game design and highlighting future research directions.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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