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This paper describes a novel method by which a spoken dialogue system canlearn to choose an optimal dialogue strategy from its experience interactingwith human users. The method is based on a combination of reinforcementlearning and performance modeling of spoken dialogue systems. The reinforcementlearning component applies Q-learning (Watkins, 1989), while the performancemodeling component applies the PARADISE evaluation framework (Walker et al.,1997) to learn the performance function (reward) used in reinforcementlearning. We illustrate the method with a spoken dialogue system named ELVIS(EmaiL Voice Interactive System), that supports access to email over the phone.We conduct a set of experiments for training an optimal dialogue strategy on acorpus of 219 dialogues in which human users interact with ELVIS over thephone. We then test that strategy on a corpus of 18 dialogues. We show thatELVIS can learn to optimize its strategy selection for agent initiative, forreading messages, and for summarizing email folders.

An Application of Reinforcement Learning to Dialogue Strategy Selection in a Spoken Dialogue System for Email