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Expert systems are consultative, highly interactive systems, and hence the quality of interaction between system and user is important for the acceptability of the system. Acquisition of data about the problem in hand is a central feature of the interaction between system and user, and it makes a major contribution to the user's perception of the system. It is therefore crucial that the acquisition of this data (both as individual data items and as a sequence of data inputs) is perceived by the user as competent. This paper identifies central, domain-independent, design goals for the information-acquisition interactions between an expert system and its user, including mixed-initiative interaction, flexibility in user input, and system competence in querying the user. The paper discusses the realisation of these goals in a diagnostic expert system, Skeletal Dysplasias Diagnostician, through an explicit data model which allows for the representation of data with temporal and spatial (locality) aspects and the decide-status function which operates on the data model.

A diagnostic framework in which there is a clear separation between the expertise per se and relevant background knowledge is discussed. We argue for the need to have an explicit representation of background knowledge. Background knowledge is domain foundational knowledge or common-sense knowledge; it is brought into play in the diagnostic context activated by the foreground knowledge, the diagnostic expertise. In the diagnostic framework discussed, background knowledge is of two types: foundational knowledge related to diagnostic findings and common-sense knowledge about time. The explicit representation and integration of expert and background knowledge is essential for achieving competent behaviour, both from the perspectives of the conversational context and the diagnostic performance of the system. The framework presented is being applied successfully to the domain of skeletal dysplasias.