Recurrent Neural Networks to Approximate the Semantics of Acceptable Logic Programs
نویسندگان
چکیده
In this paper we show that a feedforward neural network with at leastone hiddenlayer can approximate the meaning function TP for an acceptable logic program P . This is found by using the property of acceptable logic programs that for this class of programs the meaning function TP is a contraction mapping on the complete metric space of the interpretations for P as shown by Fitting in [3]. 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 iteration of the meaning function TP , that is the semantics of the logic program P .
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