Abstract
In this paper we propose a Symbolic Probability Theory for the Management of Uncertainty encoded into a Qualitative way. A semantic model of Uncertainty Representation is made with the aid of nonlogical tools built on the substrate of a M-valued Predicate Logic. In order to exploit Uncertain Knowledge, we have constructed deductive processes founded upon Logical or Conditional inferences. We have obtained Generalizations of either classical Deduction Rules, or classical Conditional Probabilities.
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Daniel, P. (1995). Qualitative reasoning under uncertainty. In: Pinto-Ferreira, C., Mamede, N.J. (eds) Progress in Artificial Intelligence. EPIA 1995. Lecture Notes in Computer Science, vol 990. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60428-6_25
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DOI: https://doi.org/10.1007/3-540-60428-6_25
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