Perspectives of Neuro--Symbolic Integration – Extended Abstract --

Authors Kai-Uwe Kühnberger, Helmar Gust, Peter Geibel

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Kai-Uwe Kühnberger
Helmar Gust
Peter Geibel

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Kai-Uwe Kühnberger, Helmar Gust, and Peter Geibel. Perspectives of Neuro--Symbolic Integration – Extended Abstract --. In Recurrent Neural Networks- Models, Capacities, and Applications. Dagstuhl Seminar Proceedings, Volume 8041, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)


There is an obvious tension between symbolic and subsymbolic theories, because both show complementary strengths and weaknesses in corresponding applications and underlying methodologies. The resulting gap in the foundations and the applicability of these approaches is theoretically unsatisfactory and practically undesirable. We sketch a theory that bridges this gap between symbolic and subsymbolic approaches by the introduction of a Topos-based semi-symbolic level used for coding logical first-order expressions in a homogeneous framework. This semi-symbolic level can be used for neural learning of logical first-order theories. Besides a presentation of the general idea of the framework, we sketch some challenges and important open problems for future research with respect to the presented approach and the field of neuro-symbolic integration, in general.
  • Neuro-Symbolic Integration
  • Topos Theory
  • First-Order Logic


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