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Tills paper describes a computational method for correcting users' miseonceptioas concerning the objects modelled by a compute," s.ystem. The method involves classifying object-related misc,mce|,tions according to the knowledge-base feature involved in the incorrect information. For each resulting class sub-types are identified, :.:cording to the structure of the knowledge base, which indicate wh:LI i.formativn may be supporting the misconception and therefore what information to include in the response. . | Correcting Object-Related Misconceptions How Should The System Respond Kathleen F. McCoy Department of Computer Information Science University of Pennsylvania Philadelphia PA 19104 Abstract This paper describes a computational method for correcting users misconceptions concerning the objects modelled by 3 computer system. The method involves classifying object-related misconceptions according to the knowledge-base feature involved in the incorrect information. For each resulting class sub-types are identified according to the structure of the knowledge base which indicate wind information may be supporting the misconception and therefore what information to include in the response. Such a characterize ion along with a model of what the user knows enables the system to reason in a domain-independent way about how best to corn t he user. 1. Introduction A major area of Al research has been the development of expert systems systems which are able to answer user s questions concerning a particular domain. Studies identifying desirable interactive capabilities for such systems Pollack ct al. 82 have found that it is not sufficient simply to allow the user to ask a question and have the system answer it. Users often want to question the system s reasoning.to make sure certain constraints have been taken into consideration and so on. Thus we must strive tu provide expert systems with the ability to interact with the user in the kind of cooperative dialogues that we see between two human conversational partners. Allowing such interactions between the system and a user raises difficulties for a Natural-Language system. Since the user is interacting with a system as s he would with a human expert s he will most likely expect the system to bfhavf as a human expert. Among other things the user will expect the system to be adhering to the cooperative principles of conversation Grice 75 Joshi 82 . If these principles are not followed by the system the user is likely to become .
