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<Paper uid="E91-1033">
  <Title>EXPLOITING CONVERSATIONAL IMPLICATURE FOR GENERATING CONCISE EXPLANATIONS</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1. INTRODUCTION
</SectionTitle>
    <Paragraph position="0"> The task of providing informative natural language explanations for illustrating the results produced by decision support systems has been gtven increased attention recently. The proposed methods preferably address tailoring of explanations to the needs of their addressees, including, for instance, object descriptions \[8\] and presentation of taxonomic knowledge \[7\].</Paragraph>
    <Paragraph position="1"> In addition, particular emphasis has been put on reactive explanation techniques for selecting an appropriate content according to contextual interpretation \[6\], and on the way of presenting explanations by taking the information Seeking person's knowledge into account \[1\].</Paragraph>
    <Paragraph position="2"> Whereas these approaches attack various issues important for the generation of natural language explanations, none of them has focussed on the conciseness of explanations in a broader context. Aiming at the production of natural and concise texts, we have concentrated our efforts on presenting different types of knowledge and their interrelations because this kind of information is typically relevant for explanations.</Paragraph>
    <Paragraph position="3"> We formally reconstruct aspects of the Gricean principle of relevance \[3\] and exploit the results obtained for creating concise explanations to questions about solutions proposed by the expert system OFFICE-PLAN \[5\]. This system is able to appropriately assign a set of employees to a set of rooms in offices, which is guided by a number of constraints expressing various kinds of the persons&amp;quot; requirements.</Paragraph>
  </Section>
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