Extended theory refinement in knowledge-based neural networks

This paper shows that single hidden layer networks with semi-linear activation function compute the answer set semantics of extended logic programs. As a result, incomplete (nonmonotonic) theories, presented as extended logic programs, i.e., possibly containing both classical and default negations, may be refined through inductive learning in knowledge-based neural networks.

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