A New Technique of Policy Trees for Building a POMDP based Intelligent Tutoring System

Abstract

Partially observable Markov decision process (POMDP) is a useful technique for building intelligent tutoring systems (ITSs). It enables an ITS to choose optimal tutoring actions when uncertainty exists. An obstacle to applying POMDP to ITSs is the great computational complexity in decision making. The technique of policy trees may improve the efficiency. However, the number of policy trees is normally exponential, and the cost for evaluating a tree is also exponential. The technique is still too expensive when applied to a practical problem. In our research, we develop a new technique of policy trees for better efficiency. The technique is aimed at minimizing the number of policy trees to evaluate in making a decision, and reducing the costs for evaluating individual trees. The technique is based on pedagogical orders of the contents in the instructional subject. In this paper, we first provide the background of ITS and POMDP, then describe the architecture of our POMDP based ITS, and then present our technique of policy trees for POMDP solving, and finally discuss some experimental results.