https://pure.iiasa.ac.at/id/eprint/13612 2021-08-27T17:41:27Z info:eu-repo/semantics/descriptiveMetadata https://pure.iiasa.ac.at/id/eprint/13612/ ALEX: automatic learning in expert systems Winkelbauer, L. Fedra, K. An environment for automatic learning (ALEX) has been designed and implemented in a modular fashion. The system consists of an example generation module (i.e., tutor software system representing the application domain); the learning subsystem; an analysis component; and the user interface and control structure integrating these components. As the core of the learning subsystem the incremental learning algorithm ID-H has been developed, based on the incremental application of hybrid clustering. To improve the overall performance of the learning environment, a feedback loop between the results of a learning step and the input of the next learning step has been introduced. The learning environment can automatically direct its learning strategy according to its assessment of its performance IEEE 1991 Book Section PeerReviewed Winkelbauer, L. <https://pure.iiasa.ac.at/view/iiasa/1668.html> & Fedra, K. <https://pure.iiasa.ac.at/view/iiasa/1241.html> (1991). ALEX: automatic learning in expert systems. In: Proceedings. The Seventh IEEE Conference on Artificial Intelligence Application. pp. 59-62 Piscataway, NJ, United States: IEEE. 10.1109/CAIA.1991.120846 <https://doi.org/10.1109/CAIA.1991.120846>. http://dx.doi.org/10.1109/CAIA.1991.120846 doi:10.1109/CAIA.1991.120846 doi:10.1109/CAIA.1991.120846 info:eu-repo/semantics/humanStartPage