An I-POMDP Based Multi-Agent Architecture for Dialogue Tutoring
Fangju Wang · 2013
Dialogue systems have been widely considered as useful tools for education.The challenging tasks in developing a dialogue tutoring system include correctly interpreting student input and choosing appropriate responses.In this paper, we present a two-agent architecture for addressing the challenges.The two agents are learner agents in a reinforcement learning algorithm, which is based on the interactive partially observable Markov decision process (I-POMDP).One agent learns user behavior for disambiguating student input, and the other learns the optimal teaching strategies.