This paper describes REGAL, a distributed genetic algorithm-based system, designed for learning first-order logic concept descriptions from examples. The system is a hybrid of the Pittsburgh and the Michigan approaches, as the population constitutes a redundant set of partial concept descriptions, each evolved separately. In order to increase effectiveness, REGAL is specifically tailored to the concept learning task; hence, REGAL is task-dependent, but, on the other hand, domain-independent. The system proved particularly robust with respect to parameter setting across a variety of different application domains. REGAL is based on a selection operator, called Universal Suffrage operator, provably allowing the population to asymptotically converge, on the average, to an equilibrium state in which several species coexist. The system is presented in both a serial and a parallel version, and a new distributed computational model is proposed and discussed. The system has been tested on a simple artificial domain for the sake of illustration, and on several complex real-world and artificial domains in order to show its power and to analyze its behavior under various conditions. The results obtained so far suggest that genetic search may be a valuable alternative to logic-based approaches to learning concepts, when no (or little) a priori knowledge is available and a very large hypothesis space has to be explored.

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Abstracf-This paper describes the results of an 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. The system, ENIGMA, used in the experimentation, 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. The application described in this paper has been selected, among several others, for its particular significance, both in terms of complexity of the solved problem and in terms of the obtained industrial benefits. The problem has been supplied by the ENICHEM Company and consists in discovering malfunctions in electromechanical apparata.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. This database layer serves the additional goal of easing the interaction between the learning module and an information system, currently used at ENICHEM to directly store the data measured in field.An expert system, MEPS, devoted to the same task, has also been manually developed. Then, a number of comparisons along different dimensions of the manual and automatic development process have been possible, allowing some practical indications to be suggested. Index Terms-Machine learning, knowledge acquisition, diagnostic expert systems.