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<Paper uid="N06-2044">
  <Title>Evolving optimal inspectable strategies for spoken dialogue systems</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We report on a novel approach to generating strategies for spoken dialogue systems. We present a series of experiments that illustrate how an evolutionary reinforcement learning algorithm can produce strategies that are both optimal and easily inspectable by human developers. Our experimental strategies achieve a mean performance of 98.9% with respect to a pre-defined evaluation metric. Our approach also produces a dramatic reduction in strategy size when compared with conventional reinforcement learning techniques (87% in one experiment). We conclude that this algorithm can be used to evolve optimal inspectable dialogue strategies.</Paragraph>
  </Section>
class="xml-element"></Paper>