GameMentor: Customized Tutorial for Video Games

被引:0
|
作者
Jouibari, Zahra Ebrahimi [1 ]
Moakhkhar, Hosein Navaei [1 ]
Reformat, Marek Z. [1 ,2 ]
机构
[1] Univ Alberta, Elect & Comp Engn Dept, Edmonton, AB, Canada
[2] Univ Social Sci, Lodz, Italy
关键词
Video games; Deep reinforcement learning; Customized tutorial; Human system interaction;
D O I
10.1109/HSI61632.2024.10613541
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Video game tutorials are crucial because they shape a player's initial experience with a game. The design of these tutorials poses a significant challenge as they can influence the continuous engagement of players. Existing tutorials often employ a one-size-fits-all approach, failing to account for individual skill variations among players. This paper introduces GameMentor, a creative Al-driven tutorial system that personalizes the learning experience for each player. GameMentor employs a deep reinforcement learning AI agent trained in gameplay to analyse gameplay data and identify player's mistakes and behaviour at critical moments. This information is then used to create customized tutorials. They allow players to practice by recreating the scenarios where they initially struggled, thereby enhancing their learning pace and overall performance. The results of our study demonstrate significant benefits of the GameMentor compared with traditional tutorials. GameMentor offers a more tailored and effective learning experience.
引用
收藏
页数:6
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