Training a real-world POMDP-based dialogue system
Blaise Thomson, Jost Schatzmann, Karl Weilhammer, Hui Ye, Steve J. Young · 2007
Partially Observable Markov Decision Processes provide a principled way to model uncertainty in dialogues. However, traditional algorithms for optimising policies are intractable except for cases with very few states. This paper discusses a new approach to policy optimisation based on grid-based Q-learning with a summary of belief space. We also present a technique for bootstrapping the system using a novel agenda-based user model. An implementation of a policy trained using this system was tested with human subjects in an extensive trial. The policy gave highly competitive results, with a 90.6% task completion rate.