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Abstract: this paper, we argue that techniques proposed for combining empirical and explanation-based learning methods can also be used to detect errors in rule-based expert systems, to isolate the blame for these errors to a small number of rules and suggest revisions to the rules to eliminate these errors. We demonstrate that FOCL, an extension to Quintan's FOIL program, can learn relational concepts in spite of an incorrect domain theory (e.g. a knowledge base of an expert system that contains some... (Update)
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...examples to drive the refinement process. We have also been using KRUST to refine corrupted versions of the student loan KB (Pazzani Brunk, 1991) which has a first order representation. KRUST s method of refinement has been generalised to cope with the complication of...
.... in knowledge base re#nement #e.g. TEIRESIAS #Davis, 1979#, theory revision #e.g. EITHER #Ourston Mooney, 1994#, FOCL #Pazzani Brunk, 1991##, apprenticeship systems #e.g. ODYSSEUS #Wilkins, 1990# and NeoDISCIPLE #Tecuci, 1992##, and Validation Veri#cation #e.g. #O...
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BibTeX entry: (Update)
Pazzani, M. J., & Brunk, C. A. (1991). Detecting and correcting errors in rule-based expert systems: An integration of empirical and explanation-based learning. Knowledge Acquisition, 3, 157--173. http://citeseer.ist.psu.edu/pazzani91detecting.html More
@techreport{ pazzani90detecting,
author = "Michael J. Pazzani and Clifford A. Brunk",
title = "Detecting and Correcting Errors in Rule-Based Expert Systems: An Integration of Empirical and Explanation-Based Learning",
number = "ICS-TR-90-38",
pages = "16",
year = "1990",
url = "citeseer.ist.psu.edu/pazzani91detecting.html" }
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