Detecting and Correcting Errors in Rule-Based Expert Systems: An Integration of Empirical and Explanation- Based Learning (1991)
| Citations: | 40 - 13 self |
BibTeX
@MISC{Pazzani91detectingand,
author = {Michael J. Pazzani and Clifford and Clifford A. Brunk},
title = {Detecting and Correcting Errors in Rule-Based Expert Systems: An Integration of Empirical and Explanation- Based Learning},
year = {1991}
}
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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 erroneous rules). A prototype knowledge acquisition tool, KR-FOCL, has been constructed that utilizes a trace of FOCL to suggest revisions to a rule base
Citations
| 23 | Classification Problem Solving - Clancey - 1984 |
| 8 | KNACK—reportdriven knowledge acquisition - Klinker, Bentolila, et al. - 1987 |
| 2 | Constructing an expert system - BARSTOW, BECHTAL, et al. - 1983 |
| 1 | Personal construct theory and the transfer of human expertise - Boost - 1984 |
| 1 | Model-directed learning of production rules - T - 1978 |
| 1 | Refnemenr of Expert System Knowledge - MARCUS, MCDERMOTT, et al. - 1985 |
| 1 | Pattern recognition as rule-guided inductive inference - MICWALSKI - 1980 |
