Author:
Fangju Wang
Affiliation:
University of Guelph, Canada
Keyword(s):
Intelligent Tutoring System, Partially Observable Markov Decision Process, Reinforcement Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Computer-Supported Education
;
e-Learning
;
Enterprise Information Systems
;
Information Technologies Supporting Learning
;
Intelligent Tutoring Systems
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.