A neural approach to extended logic programs
A neural net based development of multi-adjoint logic programming is presented. Transformation rules carry programs into neural networks, where truth-values of rules relate to output of neurons, truth-values of facts represent input, and network functions are determined by a set of general operators; the output of the net being the values of propositional variables under its minimal model. Some experimental results are reported.
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