Partially Observable Markov Decision Processes (POMDPs) are attractive for dialogue management because they are made to deal with noise and partial information. This paper addresses the problem of using them in a practical development cycle. We apply factored POMDP models to three applications.
We examine our experiences with respect to design choices and issues and compare performance with hand-crafted policies.