Abstract
A neural approach to propositional multi-adjoint logic programming was recently introduced. In this paper we extend the neural approach to multi-adjoint deduction and, furthermore, modify it to cope with abductive multi-adjoint reasoning, where adaptations of the uncertainty factor in a knowledge base are carried out automatically so that a number of given observations can be adequately explained. © 2002 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Medina, J., Mérida-Casermeiro, E., & Ojeda-Aciego, M. (2002). A neural approach to abductive multi-adjoint reasoning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2443 LNAI, pp. 213–222). Springer Verlag. https://doi.org/10.1007/3-540-46148-5_22
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