On compiling queries in recursive first-order databases
Journal of the ACM (JACM)
Learning from examples in presence of uncertainty
Approximate reasoning in intelligent systems, decision and control
Automated Concept Acquisition in Noisy Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Explanation-based learning: a problem solving perspective
Artificial Intelligence
Integrated learning in a real domain
Proceedings of the seventh international conference (1990) on Machine learning
Deduction in top-down inductive learning
Proceedings of the sixth international workshop on Machine learning
Rigel: An Inductive Learning System
Machine Learning
Use of causal models and abduction in learning diagnostic knowledge
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
An interference matching technique for inducing abstractions
Communications of the ACM
Principles of Database Systems
Principles of Database Systems
Learning Logical Definitions from Relations
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Learning Quantitative Features in a Symbolic Environment
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Integrating Multiple Learning Strategies in First Order Logics
Machine Learning - Special issue on multistrategy learning
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Phase Transitions in Relational Learning
Machine Learning
Relational learning as search in a critical region
The Journal of Machine Learning Research
Machine learning based feature extraction for quality control in a production line
Integrated Computer-Aided Engineering
A distributed approach for multiple model diagnosis of physical systems
Information Sciences: an International Journal
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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.