We propose a method for computing supported models of normal logic programs in vector spaces using gradient information. First, the program is translated into a definite program and embedded into a matrix representing...
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ISBN:
(纸本)9783031157073;9783031157066
We propose a method for computing supported models of normal logic programs in vector spaces using gradient information. First, the program is translated into a definite program and embedded into a matrix representing the program. We introduce a loss function based on the implementation of the immediate consequence operator T-P by matrix-vector multiplication with a suitable thresholding function, and we incorporate regularization terms into the loss function to avoid undesirable results. The proposed thresholding operation is an almost everywhere differentiable alternative to the non-linear thresholding operation. We report the results of several experiments where our method shows promising performance when used with adaptive gradient update.
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