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<Paper uid="P93-1037">
  <Title>A FLEXIBLE APPROACH TO COOPERATIVE RESPONSE GENERATION IN INFORMATION-SEEKING DIALOGUES</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
INTRODUCTION
</SectionTitle>
    <Paragraph position="0"> This paper presents a plan-based consultation system for getting information on how to achieve a goal in a restricted domain, l The main purpose of the system is to recognize the user's plans and goals to build cooperative answers in a flexible way \[Allen, 83\], \[Carberry, 90\]. The system is composed of two parts: hypotheses construction and response generation.</Paragraph>
    <Paragraph position="1"> The construction of hypotheses is based on Context Models (CMs) \[Carberry, 90\]. Carberry uses default inferences \[Carberry, 90b\] to select a single hypothesis for building the final answer of the system and, in case the choice is incorrect, a repair dialogue is started. Instead, in our system, we consider all plausible hypotheses and if the ambiguity among them is relevant for the generation of the response, we try to solve it by starting a clarification dialogue. According to \[van Beek and Cohen, 91\], clarification dialogues are simpler for the user than repair ones, because they only involve yeshlo questions on the selected ambiguous plans. Furthermore, repair dialogues generally require a stronger participation of the user. Finally, if the misunderstanding is not discovered, the system delivers information that is not proper to the user's case. For these reasons, it is preferable to solve the runbiguity a priori, by asking the user information on his intentions. In van Beek and Cohen's approach cl,'wification dialogues are started, even in case the answers associated with the plausible hypotheses are distinguished by features that could dhectly be managed in the answer. We avoid this by identifying the constraints relevant for a clarification dialogue and those which can be mentioned in the answer. In this way, the friendliness of the system is improved lThe system is concerned with information about a CS Deparunent.</Paragraph>
    <Paragraph position="2"> and the number and the length of the clarification dialogues are reduced.</Paragraph>
    <Paragraph position="3"> In the perspective of generating flexible cooperative answers, it is important to differentiate their detail level by adapting them to the user's competence in the domain. In our work, we want to study how to embed information obtained from a user model component in the system. As a first step in this direction, we introduce a preliminary classification of users in three standard levels of competence corresponding to the major users' prototypes the system is devoted to. Then, in order to produce differentiated answers, the hypotheses are expanded according to the user's competence level.</Paragraph>
    <Paragraph position="4"> The knowledge about actions and plans is stored in a plan library structured on the basis of two main hierarchies: the Decomposition Hierarchy (DH) and the Generalization Hierarchy (GH) \[Kautz and Allen, 86\].</Paragraph>
    <Paragraph position="5"> The first one describes the plans associated with the actions and is used for explaining how to execute a complex action. The second one expresses the relation among genera/and specific actions (the major specificity is due to additional restrictions on parameters). It supports an inheritance mechanism and a top-down form of clarification dialogue.</Paragraph>
  </Section>
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