Rule groupings in expert systems

Abstract

Currently, expert system shells do not address software engineering issues for developing or maintaining expert systems. As a result, large expert systems tend to be incomprehensible, difficult to debug or modify, and almost impossible to verify or validate Partitioning rule-based systems into rule groups which reflect the underlying subdomains of the problem should enhance the comprehensibility, maintainability, and reliability of expert-system software. In this paper, we investigate methods to semi-automatically structure a CLIPS rule base e into groups of rules that carry related information. We discuss three different distance metrics for measuring the relatedness of rules and describe two clustering algorithms based on these distance metrics. The results of our experiment with three sample rule bases are also presented.