Living Up to Expectations: Computing Expert Responses

LIVING UP TO EXPECTATIONS: COMPUTING EXPERT RESPONSES' Aravind Joshi and Bonnie Webber Department of Computer and Information Science Moore School/D2 University of Pennsylvania Philadelphia PA 19104 Ralph M. Weischedel 2 Department of Computer & Information Sciences University of Delaware Newark DE 19716 ABSTRACT In cooperative man-machine interaction, it is necessary but not sufficient for a system to respond truthfully and informatively to a user's question. In particular, if the system has reason to believe that its planned response might mislead the user, then it must block that conclusion by modifying its response. This paper focusses on identifying and avoiding potentially misleading responses by acknowledging types of "informing behavior" usually expected of an expert. We attempt to give a formal account of several -types of assertions that should be included in response to questions concerning the achievement of some goal (in addition to the simple answer), lest the questioner otherwise be misled. 1. Introduction] In cooperative man-machine interaction, it is necessary but not sufficient for a system to respond truthfully and informatively to a user's question. In particular, if the system has reason to believe that its planned response might mislead the user to draw a false conclusion, then it must block that conclusion by modifying or adding to its response. Such cooperative behavior was investigated in [5], in which a modification of Grice% Mazim of Quality - "Be truthful" - is proposed: If you, the speaker, plan to say anything which may imply for the hearer something that you believe to be false, then provide further information to block it. This behavior was studied in the context of interpreting certain definite noun phrases. In this paper, we investigate this revised principle as applied to responding to users' plan-related questions. Our overall aim is to: 1. characterize tractable cases in which the system as respondent (R) can anticipate the possibility of the user/questioner (Q) drawing false conclusions from its response and hence alter it so as to prevent this happening; 2. develop a formal method for computing the projected inferences that Q may draw from a lThis work is partially supported by NSF Grants MCS 81.-07200, MCS 83-.052"21, Lad [ST 8~11400. 2At present visiting the Depm.rtment of Computer ud lrdrormation Science, University of Pennsylvania PA 10104. 179 particular response, identifying those factors whose presence or absence catalyzes the inferences; 3. enable the .system to generate modifications of its response that can defuse possible false inferences and that may provide additional useful information as well. In responding to any question, including those related to plans, a respondent (R) must conform to Grice's first Maxim of Quantitlt as well as the revised Maxim of Quality stated above: Make your contribution as informative as is required (for the current purposes of the exchange). At best, if R's response is not so informative, it may be seen as uncooperative. At worst, it may end up violating the revised Maxim of Quality, causing Q to conclude something R either believes to be false or does not …

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