ENIGMA: A System That Learns Diagnostic Knowledge.

Attilio Giordana, Lorenza Saitta, Francesco Bergadano, Filippo Brancadori, Davide De Marchi: ENIGMA: A System That Learns Diagnostic Knowledge. IEEE Trans. Knowl. Data Eng. 5(1): 15-28(1993)
  author    = {Attilio Giordana and
               Lorenza Saitta and
               Francesco Bergadano and
               Filippo Brancadori and
               Davide De Marchi},
  title     = {ENIGMA: A System That Learns Diagnostic Knowledge},
  journal   = {IEEE Trans. Knowl. Data Eng.},
  volume    = {5},
  number    = {1},
  year      = {1993},
  pages     = {15-28},
  ee        = {db/journals/tkde/GiordanaSBBM93.html},
  bibsource = {DBLP,}


The results of extensive experimentation aimed at assessing the concrete possibilities of automatically building a diagnostic expert system, to be used in-field in an industrial domain, by means of machine learning techniques, are described. The system, ENIGMA, is an incremental version of the ML-SMART system, which acquires a network of first-order logic rules, starting from a set of classified examples and a domain theory. An application is described that consists of discovering malfunctions in electromechanical apparatus. ENIGMA's efficacy in acquiring sophisticated knowledge and handling complex structured examples is largely due to its underlying database management system, which supports the learning operators, defined at the abstract level, with a set of primitives, taken from the field of deductive databases. An expert system, MEPS, devoted to the same task, has also been manually developed. A number of comparisons along different dimensions of the manual and automatic development process have been possible, allowing some practical indications to be suggested.

Copyright © 1993 by The Institute of Electrical and Electronic Engineers, Inc. (IEEE). Abstract used with permission.

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