A fully connectionist model generator for covered first-order logic programs

Abstract

We present a fully connectionist system for the
\nlearning of first-order logic programs and the generation
\nof 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