Modelling uncertain positive and negative reasons in decision aiding
Decision Support Systems
Interpreting GUHA Data Mining Logic in Paraconsistent Fuzzy Logic Framework
ADT '09 Proceedings of the 1st International Conference on Algorithmic Decision Theory
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The root of this work is in Belnap’s four valued paraconsistent logic [2]. Based on a related study of Perny andTsoukias [11], we introduce para consistent Pavelka style fuzzysentential logic. Restricted to Lukasiewicz t–norm, ourapproach and the approach in [11] partly overlap; the maindifference lies in the interpretation of the logical connectivesimplication and negation. The essential mathematical tool proved inthis paper is a one–one correspondence between evidencecouples and evidence matrices that holds in all injectiveMV–algebras. Evidence couples associate to each formulaα two values a and b that can be interpreted as the degreesof pros and cons for α, respectively. Four values t, f, k, u,interpreted as the degrees of truth, falsehood, contradiction andunknowness of α, respectively, can be calculated. In such anapproach truth and falsehood are not each others complements. Thispaper can be seen as a solution to some open problems presented in[11].