  Designing the dialogue policy of a spoken dialogue system involves many
nontrivial choices. This paper presents a reinforcement learning approach for
automatically optimizing a dialogue policy, which addresses the technical
challenges in applying reinforcement learning to a working dialogue system with
human users. We report on the design, construction and empirical evaluation of
NJFun, an experimental spoken dialogue system that provides users with access
to information about fun things to do in New Jersey. Our results show that by
optimizing its performance via reinforcement learning, NJFun measurably
improves system performance.
