Séminaires Séminaires du GDR IA

Nous sommes heureux de vous annoncer le lancement des séminaires
mensuels du GDR IA, qui verront intervenir chaque mois un orateur sur
un sujet différent, proposés par les groupes de travail du GDR ou les
membres du comité scientifique.

Le but de ces exposés est de mettre en lumière, au sein d’un exposé
accessible à la plus grande partie de la communauté, les domaines
variés couverts par le GDR et ses activités. Si la langue des slides
et du discours sont laissés au choix de l’orateur, notre préconisation
par défaut sont des slides en anglais avec le libre choix de la langue
utilisée.

Liste des séminaires:

 

07/07/21 – 15h : Dominik Peters

Titre: Proportional Participatory Budgeting

 

We study voting rules for participatory budgeting, where a group of
voters collectively decides which projects should be funded using a
common budget. Many city governments around the world now use
participatory budgeting to allow their residents to influence how the
city spends its budget. Most cities handle such elections using the
most intuitive voting rule: greedily filling a knapsack with projects
that received the highest number of votes. We argue that this is a bad
voting rule, because it can ignore the preferences of substantial
minorities. Instead, we advocate the use of voting rules that are
proportional: every voter should have roughly equal influence on the
final budget, and thus different interests should be represented in
proportion to the number of their supporters. We formalize the notion
of proportionality as an axiom and design a simple and attractive
voting rule that satisfies our formal axiom, and that can be evaluated
in polynomial time. We also prove that a large class of voting rules
based on optimization cannot achieve proportional outcomes: this is
surprising because in many other contexts, researchers have found
specific objective functions that lead to fair outcomes.

Based on joint work with Grzegorz Pierczyński and Piotr Skowron
(https://arxiv.org/abs/2008.13276).

 

 

16/06/21 – 11h : Pierre Talbot

Titre: Interprétation abstraite de la programmation par contraintes

Résumé: Durant ce séminaire, nous nous intéressons à la fusion de deux domaines de recherche: la programmation par contraintes (PPC) et l’interprétation abstraite. La PPC est un paradigme déclaratif permettant de trouver les solutions d’un système de contraintes. La PPC s’applique à de nombreux domaines tels que l’ordonnancement, les tournées de véhicules ou encore en bioinformatique et composition musicale. L’interprétation abstraite est une théorie pour l’analyse statique de programmes par approximation de leurs sémantiques. Les idées véhiculées par l’interprétation abstraite se sont démontrées applicables en dehors de l’analyse de programmes, notamment pour la PPC. Au lieu d’approximer la sémantique d’un programme, nous pouvons approximer la sémantique d’une formule logique, représentant l’ensemble des solutions d’un problème de contraintes. Un des avantages d’utiliser une telle formalisation est de réutiliser des outils de l’interprétation abstraite pour la combinaison de solveur par contraintes, capturant différents langages de contraintes. Nous motiverons et présenterons plusieurs combinaisons génériques de solveurs par contraintes, afin de résoudre des problèmes d’ordonnancement. Finalement, nous aborderons un sujet de recherche en cours, explorant l’interprétation abstraite pour la parallélisation de la PPC sur GPU. Le séminaire ne requiert pas de prérequis en PPC ni interprétation abstraite.

 

26/05/21 – 11h : Anne Siegel

Titre: Modelling unconventional biological systems:
dynamical systems and/or reasoning?

Résumé: In the last years, data sciences have shown their interest
(and somehow their limitation) in life sciences to extract information
from multi-scale, incomplete, heterogeneous but somehow interdependent
data. As an important bottleneck, figuring out how data sciences
techniques can assist life sciences to provide explainable evidences
of the limitations of current knowledge in biology and therefore lead
to news discoveries.
An example of such limitations is the study of biological systems in
molecular and cellular biology. Such systems are highly difficult to
model with a single calibrated numerical model: state of the art
methods require a precise understanding of interactions occurring in
the considered species, an information which is not available for
complex organisms (for instance, eukaryotes) and hardly cultivable
bacteria such as those evidenced in microbiomes.
In this talk, we will introduce a strategy to assist biological
discovery by identifying features of large-scale unconventional
biological systems  at the molecular scale despite lacks of data. We
rely on the different reasoning modes (projection, union,
intersection) combinatorial optimization) of Answer Set Programming,
on its semantics of negation and on its combinatorial problem
optimization feature to search for new functionalities of biological
systems. In practice, we use this formalism to reason on
over-approximations of the biological system response with
steady-states of Boolean networks, in order to identify biological
candidates (enzymes, biological functions, or species) involved in
targeted biological features.
We will illustrate this approach on the emerging field of systems
ecology, which aims at understanding interactions between a consortium
of microbes and a host organism, more precisely by discussing the
putative role of host-bacterial interactions in an algal system.

 

  • La vidéo de cet exposé:

 

 

28/04/21 – 11h : Joao Marques-Silva

Titre: Formal Reasoning Methods in Explainable AI

Résumé: The forecasted applications of machine learning (ML) in safety
critical applications hinge on systems that are robust in their
operation and that can be trusted. This talk overviews recent efforts
on applying automated reasoning tools in explaining non-interpretable
(black-box) ML models. Moreover, the talk details the computation of
rigorous explanations of black-box models, and how these serve for
assessing the quality of widely used heuristic explanation approaches.
The talk also covers important properties of rigorous explanations,
namely duality properties between different kinds of explanation.
Finally, the talk briefly overviews ongoing work on mapping tractable
explainability.

07/04/21 – 11h : Loïc Paulevé

Titre: Symbolic learning of ensembles of Boolean networks predictive for cell
fate decision

Résumé: In this talk, I’ll give a global overview of challenges related to the
learning of dynamical models of cellular differentiation processes.
I’ll present on-going work mixing formal methods for modelling dynamical
systems and model synthesis from constraints on expected emerging
behaviors. This will be illustrated in the scope of the synthesis and
analysis of ensembles of Boolean automata networks using Answer-Set
Programming from data relating to the structure of the network and
observations of the system.