Affective dialogue management using factored POMDPs

Partially Observable Markov Decision Processes (POMDPs) have been demonstrated empirically to be good models for robust spoken dialogue design. This chapter shows that such models are also very appropriate for designing affective dialogue systems. We describe how to model affective dialogue systems using POMDPs and propose a novel approach to develop an affective dialogue model using factored POMDPs. We apply this model for a single-slot route navigation dialogue problem as a proof of concept. The experimental results demonstrate that integrating user’s affect into a POMDP-based dialogue manager is not only a nice idea but also helpful for improving the dialogue manager performance given that the user’s affect influences their behavior. Further, our practical findings and experiments on the model tractability are expected to be helpful for designers and researchers who are interested in practical implementation of dialogue systems using the state-of-the-art POMDP techniques.