Steps Toward Automating Knowledge Acquisition for Expert Systems

This paper presents a learning-based approach to the automation of knowledge acquisition for expert systems. An expert system is viewed as an explicit model of a human expert's competence and performance. We distinguish three phases in the development of such a model. The first one consists of defining a framework for the model, in terms of a knowledge representation formalism and an associated problem solving method. The second phase consists of defining a preliminary model that describes the basic concepts of the expertise domain. The last phase consists of incrementally extending and improving the domain model through learning from the human expert. The paper describes the learning system NeoDISCIPLE which illustrates the usefulness of six principles for automating the knowledge acquisition process: expert system building as a three-phase modeling of human expertise, understanding-based knowledge extension, knowledge acquisition through multistrategy learning, consistency-driven concept formation and refinement, closed-loop learning, and cooperation between the human expert and the learning system.