Abstract
A neural approach to propositional multi-adjoint logic programming was recently introduced. In this paper we extend the neural approach to multiadjoint 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.
Partially supported by Spanish DGI project BFM2000-1054-C02-02.
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© 2002 Springer-Verlag Berlin Heidelberg
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Medina, J., Mérida-Casermeiro, E., Ojeda-Aciego, M. (2002). A Neural Approach to Abductive Multi-adjoint Reasoning. In: Scott, D. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2002. Lecture Notes in Computer Science(), vol 2443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46148-5_22
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DOI: https://doi.org/10.1007/3-540-46148-5_22
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