Designing the dialogue strategy of a spoken dialogue system involves many nontrivial choices. This paper presents a reinforcement learning approach for automatically optimizing a dialogue strategy that addresses the technical challenges in applying reinforcement learning to a working dialogue system with human users. We then show that our approach measurably improves performance in an experimental system.
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Litman et al. (2000) studied this question.