POMDP Framework for Building an Intelligent Tutoring System

Fangju Wang

2014

Abstract

When an intelligent tutoring system (ITS) teaches its human student on a turn-by-turn base, the teaching can be modeled by a Markov decision process (MDP), in which the agent chooses an action, for example, an answer to a student question, depending on the state it is in. Since states may not be completely observable in a teaching process, partially observable Markov decision process (POMDP) may offer a better technique for building ITSs. In our research, we create a POMDP framework for ITSs. In the framework, the agent chooses answers to student questions based on belief states when it is uncertain about the states. In this paper, we present the definition of physical states, reduction of a possibly exponential state space into a manageable size, modeling of a teaching strategy by agent policy, and application of the policy tree method for solving a POMDP. We also describe an experimental system, some initial experimental results, and result analysis.

Paper Citation

in Harvard Style

Wang F. (2014). POMDP Framework for Building an Intelligent Tutoring System . In Proceedings of the 6th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-020-8, pages 233-240. DOI: 10.5220/0004801702330240

in Bibtex Style

@conference{csedu14,
author={Fangju Wang},
title={POMDP Framework for Building an Intelligent Tutoring System},
booktitle={Proceedings of the 6th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2014},
pages={233-240},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004801702330240},
isbn={978-989-758-020-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - POMDP Framework for Building an Intelligent Tutoring System
SN - 978-989-758-020-8
AU - Wang F.
PY - 2014
SP - 233
EP - 240
DO - 10.5220/0004801702330240