Approximating the Semantics of Logic Programs by Recurrent Neural Networks
Applied Intelligence
Monotonic and Residuated Logic Programs
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
On a theory of probabilistic deductive databases
Theory and Practice of Logic Programming
A neural approach to extended logic programs
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
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The framework of multi-adjoint logic programming has shown to cover a number of approaches to reason under uncertainty, imprecise data or incomplete information. In previous works, we have presented a neural implementation of its fix-point semantics for a signature in which conjunctors are built as an ordinal sum of a finite family of basic conjunctors (Gödel and Łukasiewicz t-norms). Taking into account that a number of approaches to reasoning under uncertainty consider the set of subintervals of the unit interval as the underlying lattice of truth-values, in this paper we pursue an extension of the previous approach in order to accomodate calculation with truth-intervals.