Self-supervised learning for an operational knowledge-based system

This paper addresses the problems of designing a learning system for a diag nostic knowledge based system (KBS) operating in industry. Key features con sidered are: learning from noisy data; incremental learning as the database expands from empty; learning from cases where multiple faults are present; and reliable learning for an autonomous learning system in a working envir onment. Starting from the problems of the target application, a Bayesian learning s ystem is developed, incorporating careful assessment of candidate knowledge at data level and knowledge base update level. Knowledge base updating by two methods: Learning and Assessing on the Same Set of Data, and Learning a nd Assessing on Disjoint Sets of Data are examined quantitatively, Results from the learning system upon application to databases of simulated KBS dat a are presented. Initially, no data from the target KBS was available. Consequently the syst em has not undergone domain specific 'tuning'. The results presented are pr oduced by a system based on only the most basic assumptions. It is shown that the learning system can reliably improve on knowledge elic ited under ideal conditions, and that assessing candidate knowledge on a di sjoint set of cases greatly improves reliability, It is shown that very lar ge improvements can be obtained against poor quality elicited knowledge. (C ) 1998 Elsevier Science Ltd. All rights reserved.