TY - GEN
T1 - Petri Nets and Hierarchical Reinforcement Learning for Personalized Student Assistance in Serious Games
AU - Hare, Ryan
AU - Tang, Ying
N1 - Funding Information:
This work was supported in part by the National Science Foundation under Grant 1913809 and by the U.S. Department of Education Graduate Assistance in Areas of National Need (GAANN) Grant Number P200A180055.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Adaptive serious games offer a new frontier for education, especially in complex topics. However, optimal methods for in-game adaptation are still being explored to address challenges such as limited educator resources, unpredictable or limited data, or complicated implementation procedures. This work offers an adaptable framework for personalized student assistance and directing within an adaptive serious game using reinforcement learning and Petri nets. Our proposed framework can be built upon by serious game developers and researchers to create adaptive serious games for improving student learning in other domains. Building on prior work, we address the challenge of adaptive in-game content through Petri net player modelling and a multi-agent deep reinforcement learning approach to gradually learn optimal personalized assistance. Finally, we provide proof-of-concept training performance for our proposed agent using a student simulation, demonstrating that the proposed hierarchical reinforcement learning approach offers significantly (effect size r = 0.8101) improved training performance over a tabular, single-agent approach.
AB - Adaptive serious games offer a new frontier for education, especially in complex topics. However, optimal methods for in-game adaptation are still being explored to address challenges such as limited educator resources, unpredictable or limited data, or complicated implementation procedures. This work offers an adaptable framework for personalized student assistance and directing within an adaptive serious game using reinforcement learning and Petri nets. Our proposed framework can be built upon by serious game developers and researchers to create adaptive serious games for improving student learning in other domains. Building on prior work, we address the challenge of adaptive in-game content through Petri net player modelling and a multi-agent deep reinforcement learning approach to gradually learn optimal personalized assistance. Finally, we provide proof-of-concept training performance for our proposed agent using a student simulation, demonstrating that the proposed hierarchical reinforcement learning approach offers significantly (effect size r = 0.8101) improved training performance over a tabular, single-agent approach.
UR - http://www.scopus.com/inward/record.url?scp=85145660920&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85145660920&partnerID=8YFLogxK
U2 - 10.1109/ICCSI55536.2022.9970680
DO - 10.1109/ICCSI55536.2022.9970680
M3 - Conference contribution
AN - SCOPUS:85145660920
T3 - Proceedings of the International Conference on Cyber-Physical Social Intelligence, ICCSI 2022
SP - 733
EP - 738
BT - Proceedings of the International Conference on Cyber-Physical Social Intelligence, ICCSI 2022
A2 - Chen, Xuemin
A2 - Wang, Jun
A2 - Wang, Jiacun
A2 - Tang, Ying
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Cyber-Physical Social Intelligence, ICCSI 2022
Y2 - 18 November 2022 through 21 November 2022
ER -