Integrating knowledge acquisition and performance systems

Abstract

Knowledge base building environments must progress in two important directions: (i) increased participation of domain experts in the knowledge design process through new computational models and effective man-machine interfaces and (ii) automated knowledge acquisition tools to facilitate the overt expression of knowledge. This paper presents the integration of a knowledge acquisition methodology with a performance system. The resulting architecture represents a combination of techniques from psychology, cognitive sciences and artificial intelligence. New dimensions emerge from this implementation and integration both at the theoretical and practical levels. The overall system is not linked to a particular control structure and is not task dependent. We discuss the value of intermediate representations in this context and the role of different approaches to the induction process. Topological induction is particularly efficient in the elicitation process and stresses the importance of interactive inductive techniques with participation from the experts. While the knowledge acquisition tool provides an analysis and structuring of the domain knowledge, the control is implemented using the performance system's interface. Therefore, both modules participate in the overall knowledge acquisition process. Beyond the integration of these knowledge acquisition and performance systems, the architecture can also be integrated with databases, text analysis techniques, and hypermedia systems.