Methods for knowledge acquisition and refinement in second generation expert systems

Abstract

First generation expert systems rely on the use of surface knowledge, such as associational or heuristic. Second generation technology is characterized by two additional features: deep knowledge and machine learning. Three second generation methods for knowledge acquisition are reviewed: learning rules from examples, model-based rule learning, and semi-automatic model acquisition. The man-machine process of acquiring and refining knowledge extends the role of expert systems to expert support systems, since both man and machine learn through repeated knowledge refinement cycles. Explanation of solutions and of the knowledge base itself is crucial for this man-machine learning process. An extended expert system shell schema is presented that includes a knowledge acquisition and a knowledge explanation module.