A Fully Connectionist Model Generator for Covered First-Order Logic Programs
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.
& Witzel, A.
(2007). A Fully Connectionist Model Generator for Covered First-Order Logic Programs. Proceedings of the 20th International Joint Conference on Artificial Intelligence, 666-671.