Ripple-down rules with censored production rules

Authors:
Yang Sok Kim;Paul Compton;Byeong Ho Kang
Affiliations:
University of New South Wales, Sydney, New South Wales, Australia;University of New South Wales, Sydney, New South Wales, Australia;University of Tasmania, Tasmania, Australia
Venue:
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
Year:
2012

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Abstract

Ripple-Down Rules (RDR) has been successfully used to implement incremental knowledge acquisition systems. Its success largely depends on the organisation of rules, and less attention has been paid to its knowledge representation scheme. Most RDR used standard production rules and exception rules. With sequential processing, RDR acquires exception rules for a particular rule only after the rule wrongly classifies cases. We propose censored production rules (CPR), to be used for acquiring exceptions when a new rule is created using censor conditions. This approach is useful when we have a large number of validation cases at hand. We discuss inference and knowledge acquisition algorithms and related issues. The approach can be combined with machine learning techniques to acquire censor conditions.