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UBC CS TR-78-04 Summary

An Approach to the Organization of Knowledge for the Modelling of Converstion, February 1978 Gordon I. McCalla

This report describes an approach to modelling conversation. It is suggested that to succeed at this endeavour, the problem must be tackled principally as a problem in pragmatics rather than as one in language analysis alone. Several progmatic aspects of conversation are delineated and it is shown that the attempt to account for them raises a number of general issues in the representation of knowledge. .br A scheme for resolving some of these issues is presented and given computational description as a set of (non-implemented) LISP-based control structures called $\mid$LISP. Central to this scheme are several different types of objects that encode knowledge and communicate this knowledge by passing messages. One particular kind of object, the pattern expression ($\mid$PEXPR), turns out to be the most versatile. $\mid$PEXPRs can encode an arbitrary amount of procedural or declarative information; are capable, as a by-product of their message passing behaviour, of providing both a context for future processing decisions and a record of past processing decisions; and make contributions to the resolution of several artificial intelligence problems. .br Some examples of typical conversations that might occur in the general context of attending a symphony concert are then explored, and a particular model of conversation to handle these examples is detailed in $\mid$LISP. The model is goal oriented in its behaviour, and, in fact, is described in terms of four main goal levels: higher level non-linguistic goals; scripts directing both sides of a conversation; speech acts guiding one conversant's actions; and, finally, language level goals providing a basic parsing component for the model. In addition, a place is delineated for belief models of the conversants, necessary if utterances are to be properly understood or produced. The embedding of this kind of language model in a $\mid$LISP base yields a rich pragmatic environment for analyzing conversation.


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