A Fully Connectionist Model Generator for Covered First-Order Logic Programs

Sebastian Bader, Pascal Hitzler, Steffen Hölldobler, Andreas Witzel

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.