CRIL in short


Lens Computer Science Research Lab (CRIL UMR 8188) is a joint laboratory between Université d’Artois and CNRS, that has a strong research focus on Artificial Intelligence and its applications. It groups together about 70 members, including researchers, lecturers, PhD students, postdocs and administrative or technical staff.

The CRIL is a member of the Confederation of Laboratories for Artificial Intelligence Research in Europe of the regional humAIn alliance. It is funded by Ministère de l’Enseignement Supérieur et de la Recherche, CNRS, Université d’Artois and Hauts de France region.

CRIL is located in two different places in Lens: at the faculty of science and at the technical institute (IUT).

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Recently updated publications

2024 Antoine Amarilli, Pierre Bourhis, Florent Capelli, Mikaël Monet, Ranked Enumeration for MSO on Trees via Knowledge Compilation in International Conference on Database Theory (ICDT 2024), vol. 290, pp. 5:1–25:18, 2024.
2024 Florent Capelli, Oliver Irwin, Direct Access for Conjunctive Queries with Negations in International Conference on Database Theory, Schloss Dagstuhl – Leibniz-Zentrum für Informatik, vol. 27th International Conference on Database Theory (ICDT 2024), pp. 13:1-13:20, 2024.
2024 Astrid Klipfel, Yaël Fregier, Adlane Sayede, Zied Bouraoui, Vector Field Oriented Diffusion Model for Crystal Material Generation in The 38th Annual AAAI Conference on Artificial Intelligence, vol. 38, pp. 22193-22201, 2024.
2024 Caren Al Anaissy, Sandeep Suntwal, Mihai Surdeanu, Srdjan Vesic, On Learning Bipolar Gradual Argumentation Semantics with Neural Networks in 16th International Conference on Agents and Artificial Intelligence, SCITEPRESS - Science and Technology Publications, pp. 493-499, 2024.
2024 Data Arthur Marzinkowski, Salem Benferhat, Anastasia Paparrizou, Cédric Piette, On object detection based on similarity measures from digital maps in IntelliSys 2023 - Intelligent Systems Conference, vol. 1, 2024.

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PhD positions From AI to Propositional Logic: Conversion to SAT and Model Analysis

This thesis topic aims to create a bridge between artificial intelligence (AI) solutions and formal techniques of classical propositional logic. It seeks to develop generic methods for transforming AI models into propositional formulas, focusing on the satisfiability problem, known as the SAT. This approach looks to leverage the significant advancements made in the field of SAT solvers. A key aspect of this work is identifying the important features of AI models that can be efficiently translated into SAT representations.

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PhD positions Deep Graph Representation Learning on non-uniform 3D objects

This thesis focuses on deep learning, with an emphasis on learning graph representations. Graphs are widely used in many applications, providing a versatile representation for non-regular objects, including 3D meshes, as an alternative to traditional methods such as CNNs or image segmentation models like U-net. This thesis explores graph neural networks (GNNs) for modeling non-regular 3D objects, such as 3D meshes. Unlike CNNs, GNNs are designed to handle graph-type data, making them more suitable for representing 3D meshes.

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Pierre Marquis becomes AAIA Fellow

Oct 7, 2022 - 0:00 am

Pierre Marquis has been nominated Asia-Pacific Artificial Intelligence Association (AAIA) Fellow. The list of AAIA Fellows is available on the association website.

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IPMU'22 outstanding paper award for Sara Kebir

Jul 20, 2022 - 0:00 am

Sara Kebir received an outstanding student paper award during the international conference IPMU'22 (Information Processing and Management of Uncertainty in Knowledge-Based Systems) on July 13, 2022 in Milan, Italy for her article Probability Calibration Through Uncertain Information Revision co-authored with her supervisor 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. IPMU 2022. Communications in Computer and Information Science, vol 1602.

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Accepted papers at IJCAI'22

Jul 13, 2022 - 0:00 am

This year, 8 papers from CRIL will be presented at 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

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Gilles Audemard receives CAV 2021 award

Jul 24, 2021 - 0:00 am

Gilles Audemard received with 20 other researchers on July 23, 2021 the 2021 award from the international conference on Computer-Aided Verification for pioneering contributions to the foundation of the theory and practice of Satisfiability Modulo Theory (SMT). See the full announcement.

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Recruitment Learning concept representations from few examples for NLP systems

Mar 3, 2021 - 0:00 am

The CRIL (CNRS & Artois University) invites applications for an M2 Research internship of 6 months. It will focus on learning concept representations from few examples for NLP systems. Start date: April 1st, 2021. Deadline for applications: March 15th, 2021. Keywords: Natural language processing, learning concept representation, few-shot learning More details are available at : For any information or application please contact Zied Bouraoui (

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