IEEE Computational Intelligence Society, France Chapter

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We are pleased to announce that Michio Sugeno, of the Tokyo Institute of Technology, Japan, and the European Centre for Soft Computing, Spain, will give a talk entitled "Exploring Categories of Uncertainty - toward Structure of Uncertainty". The abstract for this talk is:

As a conventional concept of uncertainty, we are familiar with the 'probability' of a phenomenon initiated in 17 century. Also we often discuss the 'uncertainty' of knowledge. Recently, Fuzzy Theory has brought a hidden uncertainty, 'fuzziness', to light. Reflections on these ideas lead to a fundamental question: What kinds of uncertainty are we aware of? Motivated by this question, this study aims to explore categories and modalities of uncertainty. For instance, we have found that (i) 'form' is a category of uncertainty; (ii) 'inconsistency' is a modality of uncertainty; (iii) the inconsistency of form is one of the major uncertainties. Through the classification of adjectives implying various uncertainties, we elucidate seven uncertainties (or nine if subcategories are counted) and identify three essential ones among them, such as the fuzziness of wording. Finally the structure of uncertainty will be shown. The obtained structure is verified by psychological experiments, while the validity of three essential uncertainties is examined by linguistic analysis.


This coming autumn, we will also have Ronald R. Yager (Machine Intelligence Institute, Iona College, US) who will give a seminar on "Multi-Source Uncertain Information Fusion Using Measures".
The abstract of the presentation is the following:

We are interested in the problem of multi-source information fusion in the case when the information provided has some uncertainty. We note that sensor provided information as well as statistical type information generally are usually expressed in terms of a probabilistic type of uncertainty whereas linguistic information typically introduces a possibilistic type of uncertainty. More generally we are faced with a problem in which we must fuse information with different types of uncertainty. In order to provide a unified framework for the representation of these different types of uncertain information we use a set measure approach for the representation of uncertain information. We discuss a set measure representation of uncertain information. fusion problem, in addition to having a collection of pieces of information that must be fused, we need to have some expert provided instructions on how to fuse these pieces of information. Generally these instructions can involve a combination of linguistically and mathematically expressed directions. In the course of this work we begin to consider the fundamental task of how to translate these instructions into formal operations that can be applied to our information. This requires us to investigate the important problem of the aggregation of set measures.

An up to date list of our seminars can be found here and a list of coming events is here.