Marc Pirlot

Marc Pirlot Univeristy of Mons, Belgium
Marc Pirlot, born in 1952, holds a PhD in Mathematics from Université de Mons-Hainaut, Belgium. He has been teaching operations research, statistics and decision analysis at the Faculty of Engineering, Université de Mons, since 1989. He served as president of ORBEL, the Belgian OR society (1996-1998) and as principal editor of its journal JORBEL (1992-2002). He is the author or co-author of over 80 scientific publications and 3 books which mainly focus on multicriteria decision analysis and preference modelling, multi-objective optimization, metaheuristics. He has also been involved in applied research projects with the industry and the public sector.
Preference elicitation and learning in a Multiple Criteria Decision Analysis perspective: specificities and fertilization through inter-disciplinary dialogue
Capturing, modelling and predicting preferences has become an important issue in many different disciplines, among which we may cite psychology, decision analysis, machine learning, artificial intelligence, information retrieval, social choice theory. Preferences also play a major role in applications such as marketing and electronic commerce.

Although they work with the same notion, the different communities have specific issues to deal with and they use their own methods and standards. In recent years, several workshops were organized with the aim of bringing together people working in preference related domains, yet coming from various research horizons. Let us mention for instance, the Dagstuhl Seminar 14101 on Preference Learning and the DA2PL (From Decision Analysis To Preference Learning) Workshops, the next one scheduled November 2016 in Paderborn, Germany.

In this talk, we shall first sketch the way different communities look at preference learning and contrast them with the peculiarities of Multiple Criteria Decision Analysis. We then mainly focus on the inter-relations with the Machine Learning community, aiming to identify what are the issues we have in common and what can be learned from them in a Decision Analysis perspective. We illustrate the commonalities and discrepancies between both approaches by presenting some recent research works. The last part of the talk will propose and describe four research avenues which we see as structuring the recent and forthcoming efforts regarding preference elicitation and learning in the field of multiple criteria decision analysis. In these four trends, the interactions with disciplines such as Optimization, Artificial Intelligence and Machine Learning are likely to become increasingly important.