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. 1 Motivation Three long-standing open research problems in connectionism are the questions of how to instantiate the power of symbolic computation within a fully connectionist system [Smolensky, 1987], how to represent and reason about structured objects and structure sensitive processes [Fodor and Pylyshyn, 1988], and how to overcome the propositional fixation [McCarthy, 1988], i.e. how to use connectionist systems for symbolic learning and reasoning beyond propositional logic. It has been shown that feed-forward networks are universal approximators and that artificial neural networks are Tu...

• Type: Conference
• Year: 2007
• Where: IJCAI
• Authors: Sebastian Bader, Pascal Hitzler, Steffen Hölldobler, Andreas Witzel
• Keywords: Artificial Intelligence, Feed-forward Networks, IJCAI 2007, Logic Program, Semantic Operator