30 of January, 2019
Description: The main goal of M4D2 is to bring together researchers from fields of computer science that may seem at first glance rather distant but that share in fact several common points. This is particularly the case for topics pertaining to
decision making and social choice,
complex and multi-agent systems,
knowledge discovery and machine learning,
game theory and operations research, etc.
and that share the same interest in mathematical tools coming from
ordered sets and lattice theory, knowledge spaces,
clone theory and aggregation functions,
Boolean and pseudo-Boolean functions,
graph and hypergraph theory, combinatorial optimisation, etc.
With this event we hope to lay ground to fruitful discussions and expertise exchanges, and to provide a space where participants will have the chance, not only to present their recent achievements, but most importantly to share and discover different perspectives, to acquire new ideas, and to become involved in new research efforts where their expertise is welcome and needed.
M4D2 is organized by M. Couceiro and A. Napoli in the framework of the MALOTEC seminar (the French acronym for « Seminar of mathematics and logic for knowledge discovery ») held in the ORPAILLEUR Team, at LORIA.
The main topic of the 2nd symposium M4D2 will be preference modeling, reasoning and learning and it will take place on 30 of January, 2019.
All members of LORIA, IECL, CRAN and other computer science and mathematics laboratories, are most welcome!
Localization: B013, LORIA
10h00-10h45: Henri Prade (IRIT)
Title: Analogical prediction of preferences
11h00-11h45: Hélène Fargier (IRIT)
12h00-12h45: Michel Grabisch (CES, University of Paris I Panthéon-Sorbonne)
Title: Monotone Decomposition of 2-additive Generalized Additive Independence Models (Joint work with Christophe Labreuche and Mustapha Ridaoui)
Abstract: We consider discrete GAI (Generalized Additive Independence) models in decision making, begin monotone w.r.t. attributes, and study the problem of the decomposition. A canonical decomposition of a GAI is still an open problem in general, and here we propose a monotone (as a sum of nondecreasing terms) decomposition of a 2-additive discrete GAI model, which also the property to be minimal in number of terms. For this, we take advantage of the fact that discrete monotone (2-additive) GAI models are equivalent to k-ary (2-additive) capacities (monotone multichoice games), and the decomposition is nothing other than the expression of a k-ary capacity in terms of vertices of a simplex containing it. We identify the set of vertices of 2-additive k-ary capacities and give an explicit expression of the decomposition for k=2.
14h30-15h15: Sébastien Destercke (Heudiasyc)
Title: Ordinal regression with imprecise probabilities: from learning to inferences
Abstract: In this talk, we will first recall the basic principles of imprecise probabilistic approaches, as well as their motivation. We will then explore how those can be applied to the peculiar problem of ordinal regression, that we will also recall. We will focus on sets of models that present some computational advantages, both for the estimation and for the decision problems.
15h30-16h15: Alexis Tsoukiàs (LAMSADE)
16h30-17h00: Jimmy Devillet (University of Luxembourg)
Title: Generalizations of single-peakedness
Abstract: We establish a surprising connection between a family of conservative semigroups, which includes the class of idempotent uninorms, and the concepts of single-peakedness and single-plateaudness, introduced in social choice theory by D. Black. We also introduce a generalization of single-peakedness to partial orders of join-semilattices and show how it is related to the class of idempotent and commutative semigroups. Finally, we enumerate those orders when the corresponding semigroups are finite.