In memoriam: Philippe Smets (1938-2005)

نویسندگان

Hugues Bersini

Thierry Denoeux

Didier Dubois

Henri Prade

چکیده

Professor Philippe Smets passed away on Monday night November the 14th, at home with his family around. He had been suffering from a brain tumor for several months. For those interested in uncertainty modeling and handling, he was an outstanding researcher in this area, in some sense a guiding light. Philippe Smets was born in Brussels (Belgium) on November 27, 1938. He first received a medical doctor degree in 1963 from the Université Libre de Bruxelles (ULB), then a Master degree in experimental statistics from North Carolina State University, and, finally, his PhD degree in medical statistics from ULB in 1978. His PhD dissertation [21], the starting point of his research work, already contained the seeds of many of the ideas and results on belief functions that Philippe Smets was going to develop in the next two decades. Philippe Smets was the founder in 1985 of the IRIDIA laboratory (Institut de Recherches Interdisciplinaires et de Développements en Intelligence Artificielle) at ULB, and its director until he retired in 1999. Under his leadership, IRIDIA became a major Belgian research institute in Artificial Intelligence and related topics, and an internationally renowned place. Due to the unusual personality of Philippe Smets, IRIDIA was also, in the words of his present director, a very unique place to work: it was Philippe’s idea that in order to be a good place to work and think, an

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Belief Functions and the Transferable Belief Model

Belief functions have been proposed for modeling someone’s degrees of belief. They provide alternatives to the models based on probability functions or on possibility functions. There are several interpretations of belief functions: the lower probabilities model, Dempster’s model, the hint model, the probability of modal propositions model, the transferable belief model. All these models are un...

متن کامل

Decision making in the TBM: the necessity of the pignistic transformation

In the transferable belief model(TBM), pignistic probabilities are used for decision making. The nature of the pignistic transformation is justified by a linearity requirement. We justify the origin of this requirement showing it is not ad hoc but unavoidable provides one accepts expected utility theory.

متن کامل

Belief functions on real numbers

We generalize the TBM (transferable belief model) to the case where the frame of discernment is the extended set of real numbersR = [−∞,∞], under the assumptions that ‘masses’ can only be given to intervals. Masses become densities, belief functions, plausibility functions and commonality functions become integrals of these densities and pignistic probabilities become pignistic densities. The m...

متن کامل