KEYNOTE SPEAKER

Fuzzy Logic, A Tool To Cope With Subjective Information

Bernadette Bouchon-Meunier
University Paris 6, France

Abstract

Subjective information is very difficult to gather, to represent and to use because of its complexity. It is often implicit and its validity may be uncertain. It is strongly dependent on the person providing or interpreting the information. For instance, sensory information is subjective and particularly difficult to manage, because each human being has a different capability for smelling odours or seeing colours. Human agents express this information either qualitatively by means of words, with all the vagueness of natural language, or numerically through estimations or approximate values.
For these reasons, fuzzy logic provides an interesting means of dealing with subjective information, since it allows us to represent imprecise, vague or incomplete descriptions. The representation is explicit, and is given through membership functions of fuzzy sets, which can be shared by several people and modified to come to a consensus if necessary. It can also be defined according to the environment and adapted if the environment changes. Possibility theory is a tool that represents uncertainty in the framework of fuzzy logic and can be useful in representing the beliefs of human agents. Finally, aggregation methods proposed by fuzzy logic attempt to combine information about several components of sensory information, such as the size of an object recognized by means of the evaluated length and height, or to manage complex sensory variables, such as dirtiness, comfort or beauty.
Since the 1980s, fuzzy representations of subjective variables have been used extensively in industrial applications, to enable automatic controls to take complex variables into account, such as the comfort of a train or the beauty of colours in images. Social science processes, such as education or medicine, also involve subjective variables.
We will review methods to deal with subjective information in a fuzzy framework and we will illustrate their use in some case studies, such as in situations where it is possible to establish a link between subjective evaluations and objective criteria.

Biography of the Keynote Speaker

Bernadette Bouchon-Meunier is a Research Director at the National Centre for Scientific Research, and head of the Department of Databases and Machine Learning in the Computer Science Laboratory of Paris 6.
A graduate from the Ecole Normale Superieure at Cachan, she received the degrees of B.S. in Mathematics and Computer Science, Ph.D. in Applied Mathematics and D. Sc. In Computer Science from the University of Paris 6.
Editor-in-Chief of the International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (World Scientific), she is also a member of the editorial boards of the International Journal of Approximate Reasoning, Fuzzy Sets and Systems, the International Journal of Fuzzy Systems, the International Journal of Information Technology and Intelligent Computing, and the Journal of Uncertain Systems.
She is the (co)-editor of 21 books and the (co)-author of four books in French and one in Vietnamese on fuzzy logic and uncertainty management in artificial intelligence. She is a co-founder and co-executive director of the International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU), held every other year since 1986.
She is an IEEE senior member and an International Fuzzy Systems Association fellow.
Her present research interests include approximate and similarity-based reasoning, as well as the application of fuzzy logic and machine learning techniques to decision-making, data mining, risk forecasting, information retrieval and user modelling.