Title

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

Document Type

Conference Proceeding

Publication Date

1-1-2007

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.

Comments

Presented at the Twentieth International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6-12, 2007.