August 17-20, 2014, João Pessoa (Paraíba), Brazil
Invited speakers confirmed until the present moment.
Prof. Didier Dubois
Université de Toulouse, France
We try to provide a tentative assessment of the role of fuzzy sets in decision analysis. We discuss membership functions, aggregation operations, linguistic variables, fuzzy intervals and valued preference relations. The importance of the notion of bipolarity and the potential of qualitative evaluation methods are also pointed out. We take a critical standpoint on the state of the art, in order to highlight the actual achievements and try to better assess what is often considered debatable by decision scientists observing the fuzzy decision analysis literature.
Prof. Witold Pedrycz
University of Alberta, Canada
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 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 sources 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. 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) allocation of information granularity sought as an essential design asset, and (iii) emergence of higher type and higher order information granules in investigations of hierarchical architectures of systems and show their role in the analysis and synthesis of granular fuzzy systems. A class of group decision-making problems is studied in detail. We show that the reconciliation of outcomes (decisions) produced by individual fuzzy decision-making models gives rise to granular fuzzy decisions and the formation of this result invokes a mechanism of space warping and a construction of fuzzy sets of granular membership functions (being of interval-valued, fuzzy, and probabilistic character). We also investigate granular AHP models and demonstrate a pivotal role of information granularity in the generalization of these constructs.
Prof. Peter Sussner
University of Campinas, Brazil
The technical term "lattice computing" was recently coined to refer to an evolving collection of tools and mathematical models for processing lattice ordered data such as numbers, intervals, possibility and probability distributions, (fuzzy) sets, extensions of fuzzy sets as well as other types of information granules. In this context, note that many classes of information granules such as the classes of the extended integers, the extended reals, intervals, as well as classes of fuzzy sets and several of their extensions represent complete lattices that have played important roles in mathematical morphology and fuzzy set theory since many years. In the 1990’s, several researchers have started transferring operators, ideas, and concepts of mathematical morphology into the area of computational intelligence and morphological neural networks emerged as a new paradigm for computing with artificial neural networks. Other lattice computing approaches towards computational intelligence were inspired by the fuzzy ART model. Since the latter approaches towards computational intelligence rely heavily on the use of inclusion measures or fuzzy partial orders in a general lattice setting, they can also be related to mathematical morphology. We believe that lattice computing approaches will benefit from recent extensions of fuzzy mathematical morphology since type-2, interval-valued, bipolar, and intuitionistic fuzzy sets have become increasingly important in image processing / computer vision, in rule-based systems for applications in engineering and computing with words, and in approximate reasoning.