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IJCAI
2007

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

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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...
Sebastian Bader, Pascal Hitzler, Steffen Höll
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where IJCAI
Authors Sebastian Bader, Pascal Hitzler, Steffen Hölldobler, Andreas Witzel
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