POMDPs, which make it possible to learn a policy that can maximize the averaged reward in partially observable environments (Pineau et al., 2003), have been successfully adopted in task-oriented dialogue systems for learning a dialogue control module from data (Williams and Young, 2007).
no work has attempted to use POMDPs for less (or non-) task-oriented dialogue systems, such as listening agents, because user goals are not as welldefined as task-oriented ones, complicating the finding of a reasonable reward function.