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<Paper uid="H05-1127">
  <Title>Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 1011-1018, Vancouver, October 2005. c(c)2005 Association for Computational Linguistics Learning Mixed Initiative Dialog Strategies By Using Reinforcement Learning On Both Conversants</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper describes an application of reinforcement learning to determine a dialog policy for a complex collaborative task where policies for both the system and a proxy for a user of the system are learned simultaneously. With this approach a useful dialog policy is learned without the drawbacks of other approaches that require significant human interaction. The specific task that the agents were trained on was chosen for its complexity and requirement that both conversants bring task knowledge to the interaction, thus ensuring its collaborative nature. The results of our experiment show that you can use reinforcement learning to create an effective dialog policy, which employs a mixed initiative strategy, without the drawbacks of large amounts of data or significant human input.</Paragraph>
  </Section>
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