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,...
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| creator | Medina Moreno, Jesús Mérida-Casermeiro, Enrique Ojeda Aciego, Manuel |
| description | 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. |
| format | Article |
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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
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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
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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
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| title | A neural implementation of multi-adjoint logic programs via sf-homogenization |
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