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Connectionist Model generation: A First-Order Approach (2007)
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BibTeX
@MISC{Bader07connectionistmodel,
author = {Sebastian Bader and Pascal Hitzler and Steffen Hölldobler},
title = {Connectionist Model generation: A First-Order Approach},
year = {2007}
}
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Abstract
Knowledge based artificial neural networks have been applied quite successfully to propositional knowledge representation and reasoning tasks. However, as soon as these tasks are extended to structured objects and structure-sensitive processes as expressed e.g., by means of first-order predicate logic, it is not obvious at all what neural symbolic systems would look like such that they are truly connectionist, are able to learn, and allow for a declarative reading and logical reasoning at the same time. The core method aims at such an integration. It is a method for connectionist model generation using recurrent networks with feed-forward core. We show in this paper how the core method can be used to learn first-order logic programs in a connectionist fashion, such that the trained network is able to do reasoning over the acquired knowledge. We also report on experimental evaluations which show the feasibility of our approach.
Keyphrases
connectionist model generation first-order approach logical reasoning recurrent network core method declarative reading feed-forward core first-order logic program first-order predicate logic trained network core method aim knowledge representation structure-sensitive process connectionist fashion experimental evaluation neural symbolic system artificial neural network
