Abstract

This paper presents a learning-based approach to the automation of knowledge acquisition for expert systems. An expert system is viewed as an explicit mooel of a human expert's competence and perfonnance. We distinguish three phases in the development of such a model. The fIrst one consists of defIning a framework for the mooel, in terms of a knowledge representation formalism and an associated problem solving methoo. The second phase consists of defIning a preliminary mooel 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 threephase mooeling of human expertise, understanding-based knowledge extension, knowledge acquisition through multistrategy learning, consistency-driven concept fonnation and refinement, closed-loop learning, and cooperation between the human expert and the learning system.

Citations