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 50 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 or 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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Artificial Intelligence Research and Applications

mots clés du CRIL

News (RSS)

Recruitment Learning concept representations from few examples for NLP systems

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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Recruitment Post-doc Position (H/F) - Chaire IA BE4musIA (Belief Change for Multi-Source Information Analysis) - 2 years

We are offering a post-doc position in artificial intelligence.
This position is in relation with the BE4musIA project (a French «Chaire IA» project) that is dedicated to the study of tools coming from belief change theory, and more generally from knowledge representation and reasoning, for improving multi-source information analysis.



We welcome applications in any subject related to the project, in particular:

Belief Change
Belief Merging
Inconsistency handling
Inconsistency measures
Reliability assessment
Query-answering using multiple sources
Formal Ontologies
Automated knowledge base Completion
Game Theory
Social Choice Theory

BE4musIA project in short

The aim of the BE4musIA project is to use and develop tools coming from knowledge representation and reasoning (KR) in order to perform an analysis of pieces of information coming from different sources. The idea is to be able to produce at the same time an assessment of the reliability of the sources and a coherent view of the world that takes into account the received pieces of information.

This problem of trying to find the most plausible beliefs from different sources providing conflicting pieces of information is quite a frequent pattern. This is typically the case in applications in which one works with several sensors (that are typically not totally reliable), when one receives information from different agents (while some agents can be non-reliable, or, worse than that, with some agents being enemies trying to mislead you), or when one query several databases in order to answer to a query of a user.

Our aim is to study the basic abstract problem, using tools mainly coming from the knowledge representation and reasoning (KR) area, in particular methods coming from belief revision and belief merging, that formalize rational belief change, as well as inconsistency measures, which allows us to measure the extent to which certain pieces of information are in conflict with each other.


The Be4musIA (permanent) members are Sébastien Konieczny, Zied Bouraoui, Patricia Everaere, Ramon Pino Pérez, and Ivan Varzinczak.

Contact and Application

The position will be opened from 1st october 2021(flexible - this can be adapted if necessary).

For any information or application please contact Sébastien Konieczny (

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An article co-authored by CRIL's members distinguished at ECAI'2020

The list of distinguished papers at the European Conference in Artificial Intelligence (ECAI) has been unveiled on August 29, 2020, during the opening of the conference which will be a fully digital event due to COVID19.

The conference received 1363 full papers, among which 365 have been accepted for publication.

The article Consolidating Modal Knowledge Bases de Zied Bouraoui, Jean-Marie Lagniez, Pierre Marquis et Valentin Montmirail is one of the six papers to be distinguished.

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Publication of A Guided Tour of Artificial Intelligence Research

The three volumes of “A Guided Tour of Artificial Intelligence Research”
have just been published by Springer:

Knowledge Representation, Reasoning and Learning
AI Algorithms
Interfaces and Applications of AI

They constitute a large landscape of the research in artificial intelligence, with more
than 1900 pages and 53 chapters written by 144 authors.

This is the english version, completely updated and extended (by 500 pages), of a similar scope french publication
by Cépaduès.

Several members from CRIL contributed so those volumes, whose editors are Pierre Marquis, Odile Papini et Henri Prade.

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Accepted papers at IJCAI'20

This year, 8 papers from CRIL will be presented at IJCAI 2020!.

This record number of accepted papers from the lab for a single edition happened while the acceptance rate has been exceptionally low this year (12,6%).

Main track (Constraints and SAT)

On Irrelevant Literals in Pseudo-Boolean Constraint Learning
Daniel Le Berre, Pierre Marquis, Stefan Mengel, Romain Wallon

Main track (Knowledge Representation and Reasoning)

Belief Merging Operators as Maximum Likelihood Estimators
Patricia Everaere, Sebastien Konieczny, Pierre Marquis

Inconsistency Measurement for Improving Logical Formula Clustering
Yakoub Salhi

Lower Bounds for Approximate Knowledge Compilation
Alexis de Colnet, Stefan Mengel

On Computational Aspects of Iterated Belief Change
Nicolas Schwind, Sebastien Konieczny, Jean-Marie Lagniez, Pierre Marquis

Ranking Semantics for Argumentation Systems With Necessities
Dragan Doder, Srdjan Vesic, Madalina Croitoru

Main track (Data Mining)

On the Enumeration of Association Rules: A Decomposition-based Approach
Yacine Izza, Said Jabbour, Badran Raddaoui, Abdelahmid Boudane

Main track (Natural Language Processing)

Hierarchical Linear Disentanglement of Data-Driven Conceptual Spaces
Rana Alshaikh, Zied Bouraoui, Steven Schockaert

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