Abstract
In numerous real-world problems including a broad range of decision-making tasks, we are faced with a diversity of locally available distributed sources of data and expert knowledge, with which one has to interact, reconcile and form a global and user-oriented model of the system under consideration. While the technology of Soft Computing has been playing a vital and highly visible role with this regard, there are still a number of challenges inherently manifesting in these problems when dealing with collaboration, reconciliation, and efficient fusion of various sources of knowledge. To prudently address these problems, in this study, we introduce a concept of granular fuzzy systems forming an essential generalization of fuzzy systems pursued in Soft Computing. Information granularity of fuzzy sets used in these models is formalized in the framework of Granular Computing. We briefly elaborate on the fundamentals of Granular Computing including (i) a principle of justifiable granularity, (ii) an allocation of information granularity being sought as an essential design asset, and (iii) an emergence of higher type and higher order information granules in investigations of hierarchical architectures of systems. We show the roles of these principles in the analysis and synthesis of granular fuzzy systems. A class of group decision-making problems is studied in detail. We investigate granular AHP models and demonstrate a pivotal role of information granularity in the generalization of these constructs.