Le CRIL en bref
Le Centre de Recherche en Informatique de Lens (CRIL UMR 8188) est un laboratoire de l’Université d’Artois et du CNRS dont la thématique de recherche fédératrice concerne l'intelligence artificielle et ses applications. Il regroupe plus de 60 membres : chercheurs, enseignants-chercheurs, doctorants et personnels administratifs et techniques.
Le CRIL participe à la Confédération Européenne de Laboratoires en Intelligence Artificielle CLAIRE et à l'alliance régionale humAIn. Il bénéficie du soutien du Ministère de l’Enseignement Supérieur et de la Recherche, du CNRS, de l’Université d’Artois et de la région Hauts de France.
Le CRIL est localisé sur deux sites à Lens : la faculté des sciences Jean Perrin et l’IUT.
Publications récemment mises à jour
Articles de revues internationales
Articles de conférences internationales
Actualités (RSS)
Thèses proposées SAT-based Approaches for Formal Verification with B method
Candidater Summary The ANR project BLaSST targets bridging combinatorial and symbolic techniques in automatic theorem prov- ing, in particular for proof obligations generated from B models. Work will be carried out on SAT-based techniques as well as on more expressive SMT formalisms. In both cases encoding techniques, optimized resolution techniques, model generation, and lemma suggestion will be considered. Combining both lines of work, the expected scientific impact is a substantially higher degree of automation of solvers for expressive input languages by leveraging higher-order reasoning and enumerative instantiations over finite domains.
Thèses proposées Learning Interpretable Circuits
Overview In recent years, there has been a growing interest in the design of statistical learning algorithms for interpretable models, which are not only accurate but also understandable by human users. A related and desirable property is explainability, which refers to the computational ability of predictive models to explain their predictions in intelligible terms. In this setting, decision trees are of paramount importance, as they can be easily read by recursively breaking a choice into sub-choices until a decision is reached.
Prix du meilleur article étudiant IPMU'22 pour Sara Kebir
Sara Kebir a reçu le prix du meilleur article étudiant (outstanding student paper award) lors de la conférence IPMU (Information Processing and Management of Uncertainty in Knowledge-Based Systems) le 13 juillet 2022 à Milan, Italy pour son article Probability Calibration Through Uncertain Information Revision écrit avec son directeur de thèse Karim Tabia. Kebir, Sara, Tabia, Karim (2022). Classifier Probability Calibration Through Uncertain Information Revision. In Proc. of Information Processing and Management of Uncertainty in Knowledge-Based Systems.
Papiers acceptés à IJCAI'22
Cette année, ce seront 7 papiers du CRIL qui seront présentés à IJCAI 2022 !. Main track On Preferred Abductive Explanations for Decision Trees and Random Forests Gilles Audemard, Steve Bellart, Louenas Bounia, Frederic Koriche, Jean-Marie Lagniez, Pierre Marquis On the Complexity of Enumerating Prime Implicants from Decision-DNNF Circuits Alexis de Colnet, Pierre Marquis A Computationally Grounded Logic of ‘Seeing-to-it-that’ Andreas Herzig, Emiliano Lorini, Elise Perrotin Best Heuristic Identification for Constraint Satisfaction Frederic Koriche, Christophe Lecoutre, Anastasia Paparrizou, Hugues Wattez
Projet MAIA lauréat de l'AAP PIA4 ExcellenceS
L’Université d’Artois, en partenariat avec l’Université du Littoral Côte d’Opale (ULCO) et l’Université de Picardie Jules Verne (UPJV), a été sélectionnée pour le projet MAIA “Maîtriser les Applications de l’Intelligence Artificielle”, dans le cadre de la 2e vague de l’appel à projets “Excellence sous toutes ses formes” du PIA 4. Le projet figure parmi les 17 lauréats annoncés par Madame Sylvie Retailleau, Ministre de l’Enseignement Supérieur et de la Recherche.
Séminaire Hybrid AI Systems Grounded on Just−in−time Qualitative Spatio−Temporal Reasoning
Michail Sioutis - University of Bamberg
12 juil. 2022 - 14:00I describe a research roadmap for going beyond the state of the art in AI, and Qualitative Spatial and Temporal Reasoning (QSTR) in particular, and building hybrid architectures for AI that involve also robust and dynamic symbolic computation. Simply put, QSTR is a major field of study in AI that abstracts from numerical quantities of space and time by using qualitative descriptions instead (e.g., precedes, contains, is left of), with applications in a plethora of areas and domains such as smart environments, intelligent vehicles, and unmanned aircraft systems.
Séminaire Manthan: A Data-Driven Approach for Boolean Functional Synthesis.
Priyanka Golia - IIT Kanpur, Inde et NUS, Singapour
7 juil. 2022 - 15:15Given a relational specification between Boolean inputs and outputs, the problem of Boolean functional synthesis is to construct each output as a function of the inputs such that the specification is met. Synthesizing Boolean functions is one of the challenging problems in Computer Science. It has seen multiple proposals, including incremental determination, decomposition techniques from knowledge compilation, and counterexample guided refinement techniques via self-substitutions. In this talk, we will discuss Manthan, a novel data-driven approach for Boolean functional synthesis.
Séminaire Partial Robustness in Team Formation: Bridging the Gap between Robustness and Resilience.
Nicolas Schwind - AIST Tokyo
7 juil. 2022 - 14:00Team formation is the problem of deploying the least expensive team of agents while covering a set of skills. Once a team has been formed, some of the agents considered at start may be finally defective and some skills may become uncovered… I will first recall two solution concepts that have been recently introduced to deal with this issue in a proactive manner: one may form a team which is robust to changes so that after some agent losses, all skills remain covered; or one may opt for a recoverable team, i.