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,...
| Autores: | , , |
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| Formato: | artículo |
| Fecha de publicación: | 2005 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/1832 |
| Acesso em linha: | https://hdl.handle.net/2099/1832 |
| Access Level: | acceso abierto |
| Palavra-chave: | Logic programming Neural implementation Informàtica -- Matemàtica Programació lògica Xarxes neuronals (Informàtica) Classificació AMS::68 Computer science::68R Discrete mathematics in relation to computer science |
| Resumo: | 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. |
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