Some views on information fusion and logic based approaches in decision making under uncertainty

Decision making under uncertainty is a key issue in information fusion and logic based reasoning approaches. The aim of this paper is to show noteworthy theoretical and applicational issues in the area of decision making under uncertainty that have been already done and raise new open research related to these topics pointing out promising and challenging research gaps that should be addressed in the coming future in order to improve the resolution of decision making problems under uncertainty

Fuzzy Galois connections.

Abstract. The concept of Galois connection between power sets is generalized from the point of view of fuzzy logic. Studied is the case where the structure of truth values forms a complete residuated lattice. It is proved that fuzzy Galois connections are in one-to-one correspondence with binary fuzzy relations. A representation of fuzzy Galois connections by (classical) Galois connections is provided

A paraconsistent pavelka technique for multiple criteria decision analysis

Decision making involving multiple alternatives or strategies in respect of a number of conflicting criteria is usually a daunting problem for practitioners from across diverse fields. As a result, academics, particularly decision science researchers have been working so hard over the years to develop appropriate multicriteria decision making methods to aid decision makers in their quest to establish the optimum decisions in their respective fields and problems. In this paper, a technique for decision making in environments characterised by the availability of incompletely ignorant facts, partially conflicting and vague information is proposed. The technique is dependent on paraconsistent logic, Pavelka style fuzzy logic and fuzzy similarity relations. To demonstrate the use of the technique, it is applied to the selection of the best or optimal five energy mix from eight electricity generation sources for Ghana.acceptedVersionPeer reviewe