A neural implementation of multi-adjoint logic programs via sf-homogenization

A generalization of the homogenization process needed for the neural im-
plementation of multi-adjoint logic programming (a unifying theory to deal
with uncertainty, imprecise data or incomplete information) is presented here.
The idea is to allow to represent a more general family of adjoint pairs, but
maintaining the advantage of the existing implementation recently introduced
in [6]. The soundness of the transformation is proved and its complexity is
analysed. In addition, the corresponding generalization of the neural-like
implementation of the fixed point semantics of multi-adjoint is presented.