Learning Symbolic Inferences with Neural Networks
Helmar Gust, York Hagmayer, Kai‐Uwe Kühnberger, Steven A. Sloman · eScholarship (California Digital Library) · 2005
In this paper, we will present a theory of representing symbolic inferences of first-order logic with neural networks.The approach transfers first-order logical formulas into a variablefree representation (in a topos) that can be used to generate homogeneous equations functioning as input data for a neural network.An evaluation of the results will be presented and some cognitive implications will be discussed.