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<Paper uid="J88-3006">
  <Title>TAILORING OBJECT DESCRIPTIONS TO A USER'S LEVEL OF EXPERTISE</Title>
  <Section position="4" start_page="0" end_page="0" type="relat">
    <SectionTitle>
1.1 PREVIOUS WORK ON USER MODELING IN QUESTION
ANSWERING PROGRAMS
</SectionTitle>
    <Paragraph position="0"> In studying the factors involved in tailoring the content of an answer to a user, research to date has focused mainly on the problems of inferring and using user goals, plans, and beliefs (Appelt 1982, 1985; Carberry 1983, and this issue; McKeown et al. 1985), recognizing and dealing with misconceptions (Kaplan 1982; McCoy 1983; McCoy 1986, and this issue; Quilici et al., this issue), and superposing various stereotypes (Rich 1979). The issue addressed here differs from these because we are not concerned with the users' goals in asking the question, nor with correcting their view of the domain, but rather with providing an answer that is optimally informative (without being overwhelming) given how much the user knows about the domain. We are not interested in building a user model using stereotypes (as was Rich), but in determining an answer based on a user model involving user types. As in McCoy (1986, and this issue), we are more concerned about using information from the user model to generate an answer than building the model itself.</Paragraph>
    <Paragraph position="1"> While the need for a model of the user's domain knowledge in question answering systems has been noted by various researchers (Lehnert 1977; McKeown 1985), few programs have actually had one. The HAM-ANS system (Hoeppner et al. 1984) has a model of the user's knowledge, but this knowledge is mainly used for anaphora resolution and production. In our work, we are more interested in studying how a user's knowledge affects the content of an answer as opposed to its phrasing. Wallis and Shortliffe (1982), who have used the naive/expert distinction in providing an answer (or explanation), did so mainly by giving more or less detail, without addressing the issue of whether the level of detail was the only important factor to vary. The issue we confront in this work is identifying the role played by a user's level of knowledge in determining the content of an answer. The UNIX Consultant (UC) (Chin 1986) uses a user's knowledge level about the UNIX system to provide help to its users. UC, however, uses stereotypes for both the user and the knowledge base (set of UNIX commands). Stereotypes for the knowledge base include &amp;quot;simple&amp;quot;, &amp;quot;mundane&amp;quot;, and &amp;quot;complex&amp;quot;. UC matches the user type against the command type to decide on the answer. In this work, we are looking at a different kind of domain, the domain of complex physical objects, in which this categorization of the knowledge base is not possible. Furthermore, we would like to be able to tailor answers to users whose domain knowledge level falls anywhere along a knowledge spectrum without necessarily having to classify users in several different stereotypes.</Paragraph>
  </Section>
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