Algorithms Based on Paraconsistent Annotated Logic for Applications in Expert Systems (original) (raw)

In this chapter, we present functional algorithms based on the Paraconsistent Annotated logic for the treatment of information signals; and thus, able to perform as the main nucleus, analysis and decision within expert systems. The method presented is based on the fundamental concepts of Paraconsistent Annotated logic with annotation of two values (PAL2v). The PAL2v is a non-classic Logic that admits contradiction. In this chapter, a study using mathematical interpretation for a lattice representation is described, where algorithms and equations give an effective treatment on signal information that represent situations found in an uncertainty knowledge database. We present a logical-mathematical basis with the necessary visualization for obtaining these values for the degrees of evidence by illustrating degrees of certainty and degrees of contradiction in a PAL2v representative lattice. From these equations, algorithms are elaborated and utilized in computation models by Paraconsistent Systems for the treatment of uncertainties. It is then verified that these PAL2v algorithms possess easy implementation for a variety of computation languages. This therefore proves as a satisfactory method to view performance demands as a strong tool, capable of treating signals that generate uncertainties, as those originated from inconsistent and contradictory information. These algorithms were tested in small computational programs and are now being developed for great applications, mainly in the analysis of Overload Risk in Power Electric Systems 1 . In this chapter, some significant application examples