Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2024 IEEE Conference on Games, CoG 2024 |
| Publisher | IEEE Computer Society |
| ISBN (electronic) | 9798350350678 |
| ISBN (print) | 979-8-3503-5068-5 |
| Publication status | Published - 5 Aug 2024 |
| Event | 6th Annual IEEE Conference on Games, CoG 2024 - Milan, Italy Duration: 5 Aug 2024 → 8 Aug 2024 |
Publication series
| Name | IEEE Conference on Computatonal Intelligence and Games, CIG |
|---|---|
| ISSN (Print) | 2325-4270 |
| ISSN (electronic) | 2325-4289 |
Abstract
Balancing game difficulty in video games is a key task to create interesting gaming experiences for players. Mismatching the game difficulty and a player's skill or commitment results in frustration or boredom on the player's side, and hence reduces time spent playing the game. In this work, we explore balancing game difficulty using machine learning-based agents to challenge players based on their current behavior. This is achieved by a combination of two agents, in which one learns to imitate the player, while the second is trained to beat the first. In our demo, we investigate the proposed framework for personalized dynamic difficulty adjustment of AI agents in the context of the fighting game AI competition.
Keywords
- Dynamic Difficulty Adjustment, Fighting Game AI, Imitation Learning, Reinforcement Learning
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Software
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
Proceedings of the 2024 IEEE Conference on Games, CoG 2024. IEEE Computer Society, 2024. (IEEE Conference on Computatonal Intelligence and Games, CIG).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Personalized Dynamic Difficulty Adjustment Imitation Learning Meets Reinforcement Learning
AU - Fuchs, Ronja
AU - Gieseke, Robin
AU - Dockhorn, Alexander
N1 - Publisher Copyright: © 2024 IEEE.
PY - 2024/8/5
Y1 - 2024/8/5
N2 - Balancing game difficulty in video games is a key task to create interesting gaming experiences for players. Mismatching the game difficulty and a player's skill or commitment results in frustration or boredom on the player's side, and hence reduces time spent playing the game. In this work, we explore balancing game difficulty using machine learning-based agents to challenge players based on their current behavior. This is achieved by a combination of two agents, in which one learns to imitate the player, while the second is trained to beat the first. In our demo, we investigate the proposed framework for personalized dynamic difficulty adjustment of AI agents in the context of the fighting game AI competition.
AB - Balancing game difficulty in video games is a key task to create interesting gaming experiences for players. Mismatching the game difficulty and a player's skill or commitment results in frustration or boredom on the player's side, and hence reduces time spent playing the game. In this work, we explore balancing game difficulty using machine learning-based agents to challenge players based on their current behavior. This is achieved by a combination of two agents, in which one learns to imitate the player, while the second is trained to beat the first. In our demo, we investigate the proposed framework for personalized dynamic difficulty adjustment of AI agents in the context of the fighting game AI competition.
KW - Dynamic Difficulty Adjustment
KW - Fighting Game AI
KW - Imitation Learning
KW - Reinforcement Learning
UR - http://www.scopus.com/inward/record.url?scp=85203548892&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2408.06818
DO - 10.48550/arXiv.2408.06818
M3 - Conference contribution
AN - SCOPUS:85203548892
SN - 979-8-3503-5068-5
T3 - IEEE Conference on Computatonal Intelligence and Games, CIG
BT - Proceedings of the 2024 IEEE Conference on Games, CoG 2024
PB - IEEE Computer Society
T2 - 6th Annual IEEE Conference on Games, CoG 2024
Y2 - 5 August 2024 through 8 August 2024
ER -