Using Deep Reinforcement Learning to Build Intelligent Tutoring Systems

Ciprian Paduraru, Miruna Paduraru, Stefan Iordache

2022

Abstract

This work proposes a novel method for building agents that can teach human users actions in various applications, considering both continuous and discrete input/output spaces and the multi-modal behaviors and learning curves of humans. While our method is presented and evaluated through a video game, it can be adapted to many other kinds of applications. Our method has two main actors: a teacher and a student. The teacher is first trained using reinforcement learning techniques to approach the ideal output in the target application, while still keeping the multi-modality aspects of human minds. The suggestions are provided online, at application runtime, using texts, images, arrows, etc. An intelligent tutoring system proposing actions to students considering a limited budget of attempts is built using Actor-Critic techniques. Thus, the method ensures that the suggested actions are provided only when needed and are not annoying for the student. Our evaluation is using a 3D video game, which captures all the proposed requirements. The results show that our method improves the teacher agents over the state-of-the-art methods, has a beneficial impact over human agents, and is suitable for real-time computations, without significant resources used.

Paper Citation

in Harvard Style

Paduraru C., Paduraru M. and Iordache S. (2022). Using Deep Reinforcement Learning to Build Intelligent Tutoring Systems. In Proceedings of the 17th International Conference on Software Technologies - Volume 1: ICSOFT, ISBN 978-989-758-588-3, pages 288-298. DOI: 10.5220/0011267400003266

in Bibtex Style

@conference{icsoft22,
author={Ciprian Paduraru and Miruna Paduraru and Stefan Iordache},
title={Using Deep Reinforcement Learning to Build Intelligent Tutoring Systems},
booktitle={Proceedings of the 17th International Conference on Software Technologies - Volume 1: ICSOFT,},
year={2022},
pages={288-298},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011267400003266},
isbn={978-989-758-588-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Software Technologies - Volume 1: ICSOFT,
TI - Using Deep Reinforcement Learning to Build Intelligent Tutoring Systems
SN - 978-989-758-588-3
AU - Paduraru C.
AU - Paduraru M.
AU - Iordache S.
PY - 2022
SP - 288
EP - 298
DO - 10.5220/0011267400003266