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Abstract: The use of expert systems is becoming more and more widespread, making the need for appropriate machine learning techniques more acute to help ease the knowledge aquisition bottleneck. Additionally, the increasing number of large databases offers a vast potential for the automatic generation of new knowledge by machines and its communication to people in a comprehensible form. In response to these events, this paper provides an overview of current machine learning work with a particular emphasis on rule induction techniques. Firstly we provide a summary of existing rule induction techniques, including descriptions of the ID3 and AQ algorithms. Secondly, we review recent developments in rule induction technology which overcome some of the practical limitations of these basic algorithms including noise handling, probabilistic classification, large data sets and incremental learning. Finally, we describe the state of current research in machine learning and the directions in which it is heading, addressing the difficult problems of constructive induction and representation change.