Ongoing projects

BElief change FOR better MUlti-Source Information Analysis The aim of this project is 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.

EXPlainable artificial intelligence: a KnowlEdge CompilaTion FoundATION The EXPEKCTATION project is about explainable and robust AI. It aims to devise global model-agnostic approaches for interpretable and robust machine learning using knowledge compilation: we seek for generic pre-processing techniques capable of extracting from any black-box predictor a corresponding white-box which can be used to provide various forms of explanations and to address verification queries. In the project, we plan to focus on the post hoc interpretability issue: one will consider ML models that are not intrinsically interpretable and analyze the models once they have been trained.

Event-centric Reasoning for Interpreting everydAy NArratives Making sense of everyday narratives is a very difficult task, requiring a deep understanding of language and a broad knowledge of the world. While a “human reader” can rely on high-level reasoning to draw conclusions, current models largely lack this ability. The majority of existing approaches to language understanding, especially those based on end-to-end neural models, focus primarily on performing low-level (sentence-level) forms of reasoning to accomplish tasks.

An argumentation-based platform for e-democracy E-democracy is a form of government that allows everybody to participate in the development of laws. It has numerous benefits since it strengthens the integration of citizens in the political debate. Several online platforms exist; most of them propose to represent a debate in the form of a graph, which allows humans to better grasp the arguments and their relations. However, once the arguments are entered into the system, little or no automatic treatment is done by such platforms.

Collecting, Representing, cOmpleting, merging and Querying heterogeneous and UncertaIn waStewater and stormwater network data Public and private stakeholders of the wastewater and stormwater sectors are increasingly confronted with the analysis of massive heterogeneous data (imprecise and uncertain geographical data, digital/analogue maps, etc). Obtaining accurate and updated information on the underground wastewater networks is a cumbersome task especially in cities undergoing urban expansion. This multidisciplinary project CROQUIS, designed through four work packages, aims to create a framework where researchers from Water Sciences and Artificial Intelligence will join forces in order to offer novel solutions for representing, merging, archiving, classifying, integrating domain knowledge, repairing and querying heterogeneous data capturing the main characteristics of wastewater and stormwater networks.

EXplainable and parsimonious Preference models to get the most out of Inconsistent DAtabases Nowadays, data is increasingly becoming a commodity of tremendous value for many real-world domains. Due to the heterogeneity and the imprecision of sources, gathered information is often incomplete and inconsistent. For example, social network users can easily post their observations over some events, but unfortunately such posts are frequently far from being precise or correct. Additionally, different users may report on the same event from a different point of view, consequently creating inconsistent information in the network.

THeory and Evidence to Measure Influence in Social structures Joint PRC with LAMSADE (PI) and LIP6. The THEMIS project aims at producing a general ordinal theory of cooperative interaction situations and power indices for the formulation of a portfolio of social ranking solutions applied to different domains of artificial intelligence, such as decision theory, game theory, computational social choice and multi-agent systems. In particular, the project will focus on the following families of problems: 1) the axiomatic design of novel social ranking solutions accompanied with a road-map of principles guiding users to the most adapted scenario; 2) the impact of the computational difficulties of algorithms for social ranking and their vulnerability to strategic behaviour; 3) the dynamics of coalition formation and the effect of social ranking solutions on the behaviour of individuals to form stable coalition structures; 4) the application of compact preference representation to efficiently compute social ranking solutions and to assess their robustness to changes and to manipulations.

Enhancing B Language Reasoners with SAT and SMT Techniques This is a joint project with INRIA Nancy (PI), ULiège and Clearsy. The BLaSST project targets bridging combinatorial and symbolic techniques in automatic theorem proving, in particular for proof obligations generated from B models. It focuses on advancing the state of the art in automated reasoning, in particular SAT and SMT techniques, and on making these techniques available to software verification. More specifically, encoding techniques, optimized resolution techniques, model generation, and lemma suggestion will be considered.

Efficient Query answering Under UpdateS Efficient query answering, i.e., computing the answer to a query on a given database, is one of the core problems studied in database theory. It is a very fruitful area of research with a long history and many new results and directions, e.g. efficient algorithms for aggregation, enumeration of query answers, and provenance computation. Although in practice databases are dynamic objects changing over time, the theoretical research on this topic has largely focused on static databases: when the database changes even slightly, the algorithms have to be rerun from scratch before answering, losing all already computed information.

Partners : UCCS, LML, LEM, CDEP Within the ANR project (Doctoral contracts in artificial intelligence - establishment), the programme strategy of the University of Artois is to promote and conduct core research on explainable AI as well as quality interdisciplinary research on important issues in Social Sciences and Chemistry. Five PhD contracts have been supported by the ANR, and two additional ones by the University of Artois.

Modern Approaches to Knowledge Compilation MAKC is an international research project (IRP) shared between the Automated Reasoning Group of the University of California at Los Angeles (UCLA) and the Centre de Recherche en Informatique de Lens (CRIL UMR 8188 CNRS - Artois University). It has been created in 2020 for five years and is funded by CNRS, the University of California and Artois University. The MAKC IRP is centered on knowledge compilation (KC) for problem solving.

uSing argUmentation foR Fact-checkING Some grand challenges facing humanity include climate change and health. Unfortunately, implementing effective solutions for these challenges is counteracted by misinformation, disinformation, and malinformation (MDM), e.g., climate change denying or anti-vaccine propaganda. The team proposes robust and holistic fact verification methods to address this issue. The proposed methods can reduce data bias, aggregate information across multiple statements, and yield global conclusions. While humans can utilize background and domain knowledge to argue about the veracity of a fact, computers do not normally have access to such information.

Partners: INRIA, Université de Lille, IMT Douai, CNRS, UPHF, ULCO, UPJV The CornelIA project of the Hauts-de-France CPER (State-Region Plan Contract) aims to lay the foundations for responsible and sustainable AI, with scientific responses to this major challenge, a multidisciplinary work of co-construction and a in a progressive situation. It has four axes: the theoretical and scientific bases of AI, embedded AI and societal issues, multidisciplinary links and applications, and the socio-economic impact, mediation and the creation of a center of competence in AI.

Foundations of Trustworthy AI – Integrating Reasoning, Learning and Optimization TAILOR is an H2020 European project dedicated to trustworthy AI and funded by ICT-48-2020 ``Towards a vibrant European network of AI excellence centres’’ call. TAILOR brings together 55 European partners (industrial and academic). The purpose of TAILOR is to build the capacity of providing the scientific foundations for Trustworthy AI in Europe by developing a network of research excellence centres leveraging and combining learning, optimisation and reasoning.

STormwAteR and WastewAteR networkS heterogeneous data AI-driven management Acknowledgment: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the MSCA (Marie Skłodowska-Curie Actions, Staff Exchanges)-SE (Staff Exchanges) grant agreement No 101086252; Project title: STARWARS (STormwAteR and WastewAteR networkS heterogeneous data AI-driven management). Public and private stakeholders of the wastewater and stormwater sectors are increasingly faced with large quantities and multiple sources of information/data of different nature: databases of factual data, geographical data, various types of images, digital and analogue maps, intervention reports, incomplete and imprecise data (on locations and the geometric features of networks), evolving and conflicting data (from different eras and sources), etc.

Studying human compliance with argumentation principles: Creating a rational-based framework for overcoming polarisation This is a joint project with Belgrade University. This project is a collaboration between computer scientists and psychologists. We study the problem of polarization and conspiracy theories and aim to define a reasoning paradigm that can help humans be more rational when reasoning and discussing. More particularly, we use argumentation theory to structure how humans reason and decrease the chance of fallacies.

Partenaires : ULCO, UPJV, CNRS With a budget of 11 million euros over 10 years, the MAIA Project aims to support and deepen the new uses that have appeared in recent years in many scientific fields following the rise of artificial intelligence. The project aims to study, develop and deploy the strong interactions existing between artificial intelligence and three key application areas of the A2U alliance: health (UPJV), chemistry (materials, energy; UPJV/UArtois) and environment/sea (ULCO) as well as economic, sociological, ethical and legal aspects.

Produire des connaissances nouvelles en matière de justice juvénile et de santé mentale Le projet ERA vise à combler deux lacunes majeures dans les politiques publiques en faveur de la jeunesse au Sénégal : la justice juvénile et la santé mentale des jeunes. Toutes ces recherches sur la justice juvénile et la santé mentale des jeunes seront réalisées par une équipe pluridisciplinaire, composée de chercheurs (CNRS, IRD, ISED), de praticiens du droit et de la santé, d’une organisation de la société civile, l’Association Pour le Sourire d’un Enfant (APSE) et des acteurs du Ministère de la Justice du Sénégal.

Hyper-lieux, Crises, Migrations et Inégalités Le projet Hyper-lieux, Crises, Migrations et Inégalités (HYCI) propose d’analyser, au regard de deux crises majeures, de l’asile (2015-2016) et de la Covid-19, auxquelles s’ajoute la crise ukrainienne, les interactions entre les migrations internationales et les inégalités qui leurs sont attachées. HYCI s’intéresse à deux registres d’inégalités, inégalités spatiales-inégalités des droits ainsi qu’à leurs effets sur les migrant-e-s et les sociétés d’accueil, transitoire ou durable. Ces crises, passées ou en cours, révèlent la transformation et de la dynamique des parcours migratoires, interrogée dans les hyper-lieux.

Cryptanalyse algébrique pour la cryptographie post-quantum Projet en collaboration avec le MIS (porteur du projet) et le LIP6. Parmi les techniques bien établies en cryptanalyse, les attaques algébriques sont des méthodes permettant de décrire le schéma comme un système d’équations polynomiales, et par conséquent de réduire sa sécurité à la difficulté de résoudre le système associé. POSTCRYPTUM vise à concevoir des attaques algébriques efficaces, pour plusieurs classes de cryptosystèmes qui peuvent être modélisés comme un système polynomial binaire.

Production de schémas décisionnels pour atteindre l’efficience économique, sociétale et environnementale en matière de management de parcs de bâtiments et des infrastructures associées, en milieux urbains et péri-urbains.