Interactive induction of expert knowledge

Abstract

The process of extracting, structuring and organizing elicited knowledge (called knowledge acquisition) is a bottleneck in developing knowledge-based systems. A manual approach that elicits domain knowledge by interviewing human experts typically has problems, because the experts are often unable to articulate their reasoning rules. An automatic approach that induces knowledge from a set of training cases also suffers from the unavailability of sufficient training cases. We present an integrated approach that combines the strengths of both methods to compensate for their weaknesses. In this approach, human experts are responsible for solving problems, whereas an inductive learning algorithm is responsible for reasoning and consistency checking.