A Model For Generating Better Explanations

A Model For Generating Better Explanations Peter van Beek Department of Computer Science University of Waterloo Waterloo, Ontario CANADA N2L 3G1 Abstract Previous work in generating explanations from advice- giving systems has demonstrated that a cooperative sys- tem can and should infer the immediate goals and plans of an utterance (or discourse segment) and formulate a response in light of these goals and plans. The claim of this paper is that a cooperative response may also have to address a user's overall goals, plans, and preferences among those goals and plans. An algorithm is intro- duced that generates user-specific responses by reasoning about the goals, plans and preferences hypothesized about a user. 1. Introduction What constitutes a good response? There is general agreement that a correct, direct response to a question may, under certain circumstances, be inadequate. Pre- vious work has emphasized that a good response should be formulated in light of the user's immediate goals and plans as inferred from the utterance (or discourse seg- ment). Thus, a good response may also have to (i) assure the user that his underlying goal was considered in arriving at the response (McKeown, Wish, and Matthews 1985); (ii) answer a query that results from an inappropriate plan indirectly by responding to the underlying goal of the query (Pollack 1986); (iii) pro- vide additional information aimed at preventing the" user from drawing false conclusions because of violated expectations of how an expert would respond (Joshi, Webber, and Weischedel 1984a, 1984b). The claim of this paper is that a cooperative response can (and should) also address a user's overall goals, plans, and preferences among those goals and plans. We wish to show that an advice seeker may also expect the expert to respond in light of, not only the immediate goals and plans of the user as expressed in a query, but also in light of (i) previously expressed goals or prefer- ences, (ii) goals that may be inferred or known from the user's background, and (iii) domain goals the user may be expected to hold. If the expert's response does not consider these latter type of goals the result may mislead or confuse the user and, at the least, will not be cooperative. As one example, consider the following exchange between a student and student-advisor system. User: Can I enroll in CS 375 (Numerical Analysis)? System: Yes, but CS 375 does involve a lot of FOR- TRAN programming. You may find Eng 353 (Technical Writing) and CS 327 (AI) to be useful courses. The user hopes to enroll in a particular course to help fulfill his elective requirements. But imagine that in the past the student has told the advisor that he has strong feelings about not using FORTRAN as a pro- gramming language. If the student-advisor gives the simple response of "Yes" and the student subsequently enrolls in the course and finds out that it involves heavy doses of FORTRAN programming, the student will probably have justifiably bad feelings about the student- …

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