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<Paper uid="E89-1004">
  <Title>Dialog Control in a Natural Language System 1</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
INTRODUCTION
</SectionTitle>
    <Paragraph position="0"> The solution of complex problems frequently requires cooperation of multiple agents. A great deal of interaction is needed to identify suitable tasks whose completion contributes to attaining a common goal and to organize those tasks appropriately. In particular, this involves carrying out communicative subtasks including the transfer of knowledge, the adjustment of beliefs, expressing wants and pursuing their satisfaction, all of which is motivated by the intentions of the interacting agents \[Werner 88\]. An ambitious dialog system (be it an interface, a manipulation system, or a consultation system) which is intended to exhibit (some of) these capabilities should therefore consider these intentions in processing 1 The work described in this paper is part of the joint project WISBER, which is supported by the German Federal Ministery for Research and Technology under grant ITW8502. The partners in the project are: Nixdorf Computer AG, SCS Orbit GmbH, Siemens AG, the University of Hamburg, and the University of SaarbrOcken.</Paragraph>
    <Paragraph position="1"> the dialog, at least to the extent that is required for the particular type of the dialog and the domain of application.</Paragraph>
    <Paragraph position="2"> A considerable amount of work in current AI research is concerned with inferring intentions from utterances (e.g., \[Allen 83\], \[Carberry 83\], \[Grosz, Sidner 86\]) or planning speech acts serving certain goals (e.g., \[Appelt 85\]), but only a few uniform approaches to both aspects have been presented.</Paragraph>
    <Paragraph position="3"> Most approaches to dialog control described in the literature offer either rigid action schemata that enable the simulation of the desired behavior on the surface (but lack the necessary degree of flexibility, e. g., \[Metzing 79\]), or descriptive methods which may also include possible alternatives for the continuation of the dialog, but without expressing criteria to .~aide an adequate choice among them (e. g., \[Me~ing et al.</Paragraph>
    <Paragraph position="4"> 87\], \[Bergmann, Gerlach 87\]).</Paragraph>
    <Paragraph position="5"> Modeling of beliefs and intentions (i.e., of propositional attitudes) of the agents involved is found only in the ARGOT system \[Litman, Allen 84\]. This approach behaves sufficiently well in several isolated situations, but it fails to demonstrate a continuously adequate behavior in the course of a complete dialog. An elaborated theoretical framework is provided in \[Cohen, Perrault 79\] but they explicitly exclude the deletion of propositional attitudes. Hence, they cannot explain what happens when a want has been satisfied.</Paragraph>
    <Paragraph position="6"> In our approach we have enhanced the propositional attitudes by associating them with time intervals expressing their time of validity. This enables us to represent the knowledge about the actual state of the dialog (and also about past states) seen from the point of view of a certain agent and to express changes in the propositional attitudes - 27 occurring in the course of the dialog and to calculate their effect. This deep modeling is the essential resource for controling the progress of the conversation in approaching its overall goal, and, in particular, for determining the next subgoal in the conversation which manifests itself in a system utterance.</Paragraph>
    <Paragraph position="7"> We have applied our method in the NL consultation system WISBER (\[Horacek et al. 88\], \[Sprenger, Gerlach 88\]) which is able to participate in a dialog in the domain of financial investment.</Paragraph>
  </Section>
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