A mechanical solution of Schubert's steamroller by many-sorted resolution
Artificial Intelligence
Recursive distributed representations
Artificial Intelligence - On connectionist symbol processing
Artificial Intelligence - On connectionist symbol processing
Neural networks: a systematic introduction
Neural networks: a systematic introduction
Neural-Symbolic Learning System: Foundations and Applications
Neural-Symbolic Learning System: Foundations and Applications
Distributed representations and nested compositional structure
Distributed representations and nested compositional structure
A fully connectionist model generator for covered first-order logic programs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Although there are several attempts to resolve the obvious tension between neural network learning and symbolic reasoning devices, no generally acceptable resolution of this problem is available. In this paper, we propose a hybrid neuro-symbolic architecture that bridges this gap (in one direction), first, by translating a first-order input into a variable-free topos representation and second, by learning models of logical theories on the neural level by equations induced by this topos. As a side-effect of this approach the network memorizes a whole model of the training input and allows to build the core of a framework for integrated cognition.