Importance of rule groupings in verification of expert systemsThis paper elaborates attempts to semiautomatically structure a CLIPS expert-system rule base into groups of related rules that carry the same type of information. Different distance metrics that capture relevant information from the rules for grouping are discussed. Two clustering algorithms that partition the rule base into groups of related rules are given. Two independent evaluation criteria are developed to measure the effectiveness of the grouping strategies. Results of an experiment with three sample rule bases are presented.
Document ID
19900055621
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Mehrotra, Mala (NASA Langley Research Center; Vigyan Research Associates, Inc. Hampton, VA, United States)
Johnson, Sally C. (NASA Langley Research Center Hampton, VA, United States)