Logic programs with classical negation
Logic programming
Learning rules with local exceptions
Euro-COLT '93 Proceedings of the first European conference on Computational learning theory
Incremental acquisition of search knowledge
International Journal of Human-Computer Studies
Machine Learning
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
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The Ripple Down Rule (RDR) is a knowledge acquisition scheme which has been successfully used in large scale commercial applications. Most notable is the use of the scheme in building a knowledge base for interpreting pathology results. Unlike machine learning algorithms which construct theories for raw data, the commercial system using the RDR scheme requires some interaction with an expert to correct erroneous interpretations. However, the frequency of such interactions is very low. Furthermore, the simplicity of the interaction means that a knowledge engineer is not required when updating a knowledge base. A number of RDR variants have been devised for different applications. While Scheffer has given operational semantics to one variant of RDR, translations of RDR into classical logics has not yet been presented in the literature. This paper will show how two variants of RDR, single classification RDR (SCRDR) and multiple classification RDR (MCRDR), have a propositional or first-order core tied with case specific defaults. Such a translation will be used to highlight properties of the RDR scheme which make it successful. Similarities and differences in the two variants will be discussed and RDR revision will also be analysed.