Abstract
Hybridization of connectionist and symbolic systems is being proposed for machine learning purposes in many applications for different fields. However, a unified framework to analyse and compare learning methods has not appeared yet. In this paper, a multiagent-based approach is presented as an adequate model for hybrid learning. This approach is built upon the concept of bias.
This research is funded in part by the Commission of the European Communities under the ESPRIT Basic Research Project MIX: Modular Integration of Connectionist and Symboic Processing in Knowledge Based Systems, ESPRIT-9119, and by CYCIT, the Spanish Council for Research and Development, under the project M2D2: Metaaprendizaje en Minería de Datos Distribuida, TIC97-1343. The MIX consortium is formed by the following institutions and companies: Institute National de Recherche en Informatique et en Automatique (INRIA-Lorraine/CRIN-CNRS, France), Centre Universitaire d'Informatique (Université de Genève, Switzerland), Institute d'Informatique et de Mathématiques Appliquées de Grenoble (France), Kratzer Automatisierung (Germany), Fakultät für Informatik (Technische Universität München, Germany) and Dep. Ingeniería de Sistemas Telemáticos (Universidad Politécnica de Madrid, Spain).
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González, J.C., Velasco, J.R., Iglesias, C.A. (1999). An agent-based operational model for hybrid connectionist-symbolic learning. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0100471
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DOI: https://doi.org/10.1007/BFb0100471
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