A Fully Connectionist Model Generator for Covered
Sebastian Bader, Pascal Hitzler, Steffen Hölldobler, Andreas Witzel

Abstract:
We present a fully connectionist system for the learning of
first-order logic programs and the generation of corresponding models:
Given a program and a set of training examples, we embed the
associated semantic operator into a feed-forward network and train the
network using the examples.  This results in the learning of
first-order knowledge while damaged or noisy data is handled
gracefully.