English

Abstract

When an intelligent tutoring system (ITS) teaches its human student on a turn-by-turn base, the teaching can be modelled 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, modelling of a teaching strategy by an agent policy, application of the policy tree method for solving a POMDP, and online teaching strategy improvement. We also describe an experimental system implementing the framework, some initial experimental results, and result analysis.