RT Conference Proceedings
T1 Learning Pedagogical Policies from Few Training Data
A1 Iglesias Maqueda, Ana María
A1 Martínez Fernández, Paloma
A1 Aler, Ricardo
A1 Fernández Rebollo, Fernando
AB Learning a pedagogical policy in an Adaptive Educational System (AIES) fits as a Reinforcement Learning (RL) problem. However, to learn pedagogical policies requires to acquire a huge amount of experience interacting with the students, so applying RL to the AIES from scratch is infeasible. In this paper we describe RLATES, an AIES that uses RL to learn an accurate pedagogical policy to teach a course of Data Base Design. To reduce the experience required to learn the pedagogical policy, we propose to use an initial value function learned with simulated students, whose model is provided by an expert as a Markov Decision Process. Empirical results demonstrate that the value function learned with the simulated students and transferred to the AIES is a very accurate initial pedagogical policy. The evaluation is based on the interaction of more than 70 Computer Science undergraduate students, and demonstrates that an efficient guide through the contents of the educational system is obtained.
YR 2006
FD 2006-08-01
LK https://hdl.handle.net/10016/17532
UL https://hdl.handle.net/10016/17532
LA eng
NO [Poster of] 17th European Conference on Artificial Intelligence (ECAI'06). Workshop on Planning, Learning and Monitoring with Uncertainty and Dynamic Worlds,  Riva del Garda, Italy, August 8, 2006
NO This work was supported by the project GPS (TIN2004/07083)
DS e-Archivo
RD 2 jun. 2024