ANALYZING KNOWLEDGE-BASED SYSTEMS WITH MULTIVIEWPOINT CLUSTERING ANALYSIS

Knowledge-based systems have wide commercial applicability. However, a credible validation methodology for knowledge-based systems is curren tly lacking. Better knowledge acquisition techniques as well as better management, understanding, and enhancement of the knowledge base is c ritical to the success of any verification or validation activities. O ur research addresses the feasibility of partitioning rule-based syste ms into a number of meaningful units to enhance the comprehensibility, maintainability, and reliability of expert systems software. Prelimin ary results have shown that no single structuring principle or abstrac tion hierarchy is sufficient to understand complex knowledge bases. We therefore propose the multiviewpoint clustering analysis (MVP-CA) met hodology to provide multiple views of the same expert system. MVP-CA p rovides an ability to discover significant structures within the rule base by providing a mechanism to structure both hierarchically (from d etail to abstract) and orthogonally (from different perspectives). Her e we describe our approach to understanding large knowledge bases via MVP-CA. We demonstrate the need for MVP-CA by use of a couple of small classic rule bases, as well as a deployed knowledge-based system that navigates the space shuttle's reentry. We also discuss the impact of this approach on verification and validation of knowledge-based system s. MVP-CA provides art essential first step toward building an integra ted environment for verification and validation of knowledge-based app lications. It allows one to build reliable knowledge-based systems by suitably abstracting, structuring, and otherwise clustering the knowle dge in a manner that facilitates its understanding, manipulation, test ing, and utilization.