<?xml version="1.0" standalone="yes"?>
<Paper uid="C00-1073">
  <Title>Automatic Optimization of Dialogue Management</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Designing the dialogue strategy of a spoken dialogue system involves many nontrivial choices. This paper I)resents 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 hulnan users. \Y=e then show that our approach measurably improves performance in an experimental system.</Paragraph>
  </Section>
class="xml-element"></Paper>