The Role of Perspective in Responding to Property Misconceptions

TIIE ROLE OF PERSPECTIVE IN RESPONDING TO PROPERTY MISCONCEPTIONS • MS-CIS-85-31 May 1085 Kathleen F. McCoy Department of Computer & Information Science University of Pennsylvania Philadelphia, PA 19104 This work is partially supported by the ARO grant DAA20-84-K-0061 and by the NSF grant #MCS81-07290. This paper appears in The Proceedings of IJCAI-85, August 18-23, 1985, University of California, Los Angeles, Ca. 190 Abstract In order to adequately respond to misconceptions involving an object's properties, we must have a context-sensitive method for determining object similarity. Such a method is introduced here. Some of the necessary contextual information is captured by a new notion of object perspective. It is shown how object perspective can be used to account for different responses to a given misconception in different contexts. 191 1. Introduction As a user interacts with a database or an expert system, s/he may attribute a property or property value to an object that that object does not have. For instance, imagine the following query to a database. U. Give me the HULL-NO of all DESTROYERS whose MAST-HEIGHT is above 190. If a system were to evaluate such a query, it might find that there are no such ships in the database. The reason for this is that the user has queried a value of the property MAST-HEIGHT that it cannot have for the object DESTROYER. I term this error a property misconception. Upon encountering such a query, even a very cooperative system could only respond: S. There are no DESTROYERS in the database with a MAST-HEIGHT above 190. Would you like to try again? In most cases, however, this is not the way a human would respond. A study of human/human transcripts reveals that a human conversational partner often tries to get at the cause of the misconception and offer additional information to correct the wrong information. The additional information often takes the form of a correct query that is a possible alternative to the user's query. In this paper I describe some of the knowledge and reasoning that are necessary for a natural language interface to a database or expert system to mimic this human behavior. In the above query, since there is an object similar to a DESTROYER that has the value of HULL-NO given, the user's misconception may result from his/her confusing the two objects. Hence a reasonable response would be: S. All DESTROYERS in the database have a MAST-HEIGHT between 85 and 90. Were you thinking of an AIRCRAFT-CARRIER? Notice the strategy used to correct the misconception is to (1) deny (implicitly) the property/value given, (2} give the corresponding correct information, (3) suggest an alternative query containing the object the user may have confused with the misconception object. In other situations, a reasonable alternative query might involve the same object the user asked about, with a different property/value pair. This is the case in the following query. U. Give me the HULL-NO of all DESTROYERS whose MAST-HEIGHT is above 3500. 192 S. All DESTROYERS in the database …

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