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<p>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.</p>
diagnostic knowledge learning; ENIGMA; diagnostic expert system; machine learning; ML-SMART system; first-order logic rules; domain theory; database management system; deductive databases; MEPS; deductive databases; diagnostic expert systems; learning (artificial intelligence)

A. Giordana, L. Saitta, D. De Marchi, F. Brancadori and F. Bergadano, "ENIGMA: A System That Learns Diagnostic Knowledge," in IEEE Transactions on Knowledge & Data Engineering, vol. 5, no. , pp. 15-28, 1993.
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