Oct 19, 2006 · In this paper we define a notion of approximation for interpretations and prove that there exists a feed forward neural network (FNN) that ...
It is shown how to construct a 3-layer recurrent neural network (RNN) that computes the iteration of the meaning function T p of a given propositional logic ...
Oct 22, 2024 · In this paper, we show that certain semantic operators for propositional logic programs can be computed by feedforward connectionist networks, ...
Approximating the Semantics of Logic Programs by Recurrent Neural Networks · Semantics for disjunctive logic programs · A Fixpoint Semantics and an SLD-Resolution ...
Extending the feed forward network by recurrent connections we obtain a recurrent neural network whose iteration approximates the fixed point of TP. This result ...
In this article we consider the first order case. We define a notion of approximation for interpretations and prove that there exists a 3-layered feed forward ...
Recurrent Neural Networks to Approximate the Semantics of ...
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Recurrent Neural Networks to Approximate the Semantics of Acceptable Logic Programs In J. Slaney G. Antoniou, eds., Advanced Topics in Artificial ...
Using this result it can be shown that for an acceptable program such a network can be extended to a recurrent neural networks that is able to approximate the ...
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Turning the networks into recurrent ones allows one also to approximate the models associated with the semantic operators. Our methods depend on a well-known ...
Iterated function systems can easily be encoded using recurrent radial basis function networks, we eventually obtain connectionist systems which approximate ...