ANR CHAIRE IA BE4musIA   2020-2025
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.
ANR JCJC ERIANA   2023-2027
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.
ANR PRC AGGREEY   2023-2026
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.
ANR PRC CROQUIS   2022-2026
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.
ANR PRC EXPIDA   2023-2027
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.
ANR PRC PING/ACK   2019-2023
Preprocessing Information for Nontrivial Goals / Advanced Compilation of Knowledge Project involving the laboratories CRIL, GREYC (Caen), IRIT (Toulouse), and LaBRI (Bordeaux). Designing algorithms ensuring fast response times is a fundamental problem in Computer Science. Its significance is all the more salient for algorithms requiring frequent interactions with humans. Indeed, one faces this issue quite often in everyday life (e.g., when using applications on the Web or on a smartphone, short response-time guarantees are mandatory).
ANR PRC THEMIS   2021-2025
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.
ANR PRCE BLaSST   2022-2026
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.
ANR PRCI EQUUS   2020-2023
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.
ANR VIVAH   2020-2025
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.
ARgumentation tHeory And natural language ProceSSing fOr e-DemocracY Joint project with Prof. Mihai Surdeanu from the University of Arizona. The RHAPSSODY project proposes an automated reasoning framework that can extract, understand, and reason with complex arguments. The project is based on combining computational argumentation theory with natural language processing. The goal is to identify the most important arguments listed on debate platforms, estimate the acceptability degrees of these arguments using information mined from the web, and, using the totality of the arguments (those from the particular debate and those mined from the other web-sites), estimate the decision that will be taken.
CNRS IRP MAKC   2020-2024
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.
CPER CornelIA   2022-2027
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.
H2020 STARWARS   2023-2026
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.
H2020 TAILOR   2020-2024
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.
PIA4 MAIA   2022-2031
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.
AniAge   2016-2019
High Dimensional Heterogeneous Data based Animation Techniques for Southeast Asian Intangible Cultural Heritage Digital Content. “Marie Skłodowska-Curie Actions RISE (Research and Innovation Staff Exchange)” project, H2020-MSCA-RISE-2015 call. The project AniAge (High Dimensional Heterogeneous Data based Animation Techniques for Intangible Cultural Heritage Southeast Asian Digital Content) is a multidisciplinary project involving researchers in Artificial Intelligence and in computer animation. The project covers three main research topics: i) developing new digital animation techniques, ii) management of large and heterogeneous data and iii) an application to the intangible cultural heritage (IHC) of Southeast Asia countries.
ANR AMANDE   2013-2017
Advanced Multilateral Argumentation for DEliberation The AMANDE (Advanced Multilateral Argumentation for DEliberation) project aims at providing formal tools for designing multiparty deliberation systems. The main objective is to define and study the properties of such systems, so that designers can ensure that users and possibly interacting agents are able to identify key arguments in a deliberation process, and to fairly defend their view. While the technological tools are now available to deploy such systems, we believe there is now an urgent need to work on the theoretical foundations of such platforms.
ANR ASPIQ (in french)   2012-2017
Techniques ASP pour l’interrogation d’informations web hétérogènes multisources à grande échelle. Project involving the CRIL, LERIA (Angers), LIRMM (Montpellier) and LSIS (Marseille) laboratories. The objective of the project is to propose formal methods for querying heterogeneous multi-source information at large scale. In particular, the project consists in proposing ex- tensions of the standard ASP to represent processable sub-languages of OWL2 and to study new operations of information fusion represented by logic programs in extended ASP or by OWL2-like languages of various reliability and quality with or without uncertainty.
ANR BR4CP (in french)   2012-2015
Business Recommendation for Configurable Products. Project involving the following laboratories: CRIL (Lens), IRIT (Toulouse), LIRMM (Montpellier) and the industrialists Cameleon Software, IBM and Renault. The objective of the project is to propose formal models and algorithmic tools for online configuration applications to take into account customer preferences and implement recommendations on structured and highly combinatorial domains. See online : Project website
ANR COMSOC (in french)   2010-2013
Interdisciplinary project on computer science and economics, involving the laboratories CREME (Caen), CRIL, LAMSADE (Paris IX) and PREG (Polytechnique). 2010-2013. MSOC is a multi-disciplinary ANR gathering researchers in computer science and economics, around the issue of computational social choice. The idea is to look at the problems of social choice (in particular voting methods) from a “computational” angle, i.e. by asking questions about representation, algorithmic complexity, etc. For example, although it is known that in theory all voting methods are manipulatable, it is possible to find voting methods whose manipulation complexity is high enough that in practice it is “impossible” (difficult) to manipulate them.
ANR DAG (in french)   2009-2013
Approches déclaratives pour l’énumération de motifs intéressants. This project aims at cross-fertilization between three research areas: artificial intelligence, combinatorial algorithmics and data mining. In this context, one of the objectives is to define high-level declarative languages (logical or algebraic) to express and represent interesting pattern enumeration problems. This multi-disciplinary project gathering CRIL, LIRIS and LIMOS has allowed the emergence of a new research opening at CRIL around data mining. Many results have been obtained in this framework (see Results section).
ANR PHAC (in french)   2005-2009
Représentation, élicitation et agrégation de préférences sur des domaines combinatoires : nouvelles méthodes et applications. Project involving CRIL, ILOG (Valbonne), IRIT (Toulouse), LAMSADE (Paris IX), LIP6 (Paris VI) and ONERA-CERT (Toulouse). 2005-2009. The PHAC project focused on preference representation and exploitation; more specifically, it focused on the evaluation of preference representation languages, preference elicitation, preference aggregation and social choice, as well as robust, multi-criteria and uncertain decision making.
ANR PLACID (in french)   2007-2010
Probabilistic graphical models and description logics for alarm correlation in intrusion detection. This project is in the context of intrusion detection. It consists in providing probes with the ability to describe the observed events and to be able to reason about the alerts (correlation), while taking into account the elements of uncertainty. In particular, the project has studied new formal models of alert correlation based on the formalism of Bayesian networks and preference logics.
ANR SATAS   2016-2020
SAT as a Service Project involving the laboratories CRIL, CRIStAL (Lille), INRA (Toulouse) and LaBri (Bordeaux, principal investigator). The SATAS project is an ambitious project, which aims to advance the state of the art in massively parallel SAT solving with a particular eye to the applications driving progress in the field. The final goal of the project is to be able to provide a “pay as you go” interface to SAT solving services, with a particular focus on its power consumption.
ANR TUPLES (in french)   2010-2015
Polynomiality for understanding and extending the limits of efficient solvers. The partners of this project are IRIT, LSIS, Greyc and CRIL. The TUPLES project focuses on the effective solution of NP-complete problems, in particular in the field of artificial intelligence. The main objective is to significantly push back the limits currently observed on the efficiency of combinatorial problem solvers, while at the same time establishing a theoretical framework that seems necessary to reach this objective.
Contrat industriel avec Onyme (in french)   2009-2012
This is a contract to accompany the CIFRE thesis of Benoît Trouvilliez with the company Onyme. This contract concerns the use of textual data similarities for opinion mining and product search. In particular, Benoît Trouvilliez developed a processing chain, including various NLP and learning algorithms (supervised and unsupervised) for opinion analysis from short texts. The contract was carried out in two phases: feasibility study of the clustering phase (2009) and automatic analysis and clustering of short texts for statistical purposes (2009-2012).
CPER Data   2015-2022
Advanced data science and technologies Partners: INRIA, Université de Lille 1, Mines Douai, CNRS The DATA (Advanced data science and technologies) project of the Hauts-de-France CPER (State-Region Plan Contract) is carrying out a research program on the challenges of data and associated key digital technologies (cloud computing, big data and intensive computing) in strong synergy with the regional economic fabric. It revolves around three areas of research: Internet of Things, data and knowledge intelligence, high-performance computing and optimization.
Bandits as an AUTOnomous Machine (BAUTOM) Constraint programming has developed a wide range of effective, general-purpose methods to solve many large-scale, real-world problems that can be expressed and solved as constraint satisfaction problems (CSPs). Fields that benefit from CP technology are transportation, network management, telecommunications, bioinformatics and finance [1]. As a result, organisations and industries worldwide already exploit CP technology to solve difficult problems in design and configuration, planning and scheduling, temporal and spatial reasoning, etc.
Joint CNRS and FACEPE (Brazil) project   2017-2019
Reconciling Description Logic and Non-Monotonic Reasoning in the Legal Domain This project seeks to align Description Logics with non-monotonic formalisms in general and with the so-called Default or Defeasible Logics in particular, so that one can model dynamic situations, exceptions and other non-monotonic aspects of reasoning, such as legal reasoning and legal services argumentation. In particular, we shall investigate the barriers Description Logics are faced with; for instance, handling exceptions not explicitly contemplated in the normative literature.
Merging distributed sOurces for enhanceD quERy aNswering MODERN is a project proposal that crossovers both symbolic and numerical AI and aims to deliver robust merging methods for query-answering that are able to aggregate several and open web sources in order to get back trustful answers. In particular, the project aims to propose methods for merging that benefit from recent work on commonsense reasoning and vector space representations of entitles as tools to deal with inaccuracies encountered when aggregating information sources.
Compiling Provenance Data (CODA) PEPS JCJC INS2I 2017 impliquant le CRIL et CRIStAL UMR 9189. In this project, we will strengthen recently found links between databases and artificial intelligence, more specifically between query evaluation and knowledge compilation. The idea is that so-called provenance data that plays an important role in complex database queries can be transformed into representations that have been studied before by the artificial intelligence community. These representations then allow for tractable reasoning on the data to perform tasks from the area of databases.
Argument Patterns in computer supported cOllaborative LearNIng Computer supported collaborative learning is a pedagogical approach where learning takes place via social interaction using a computer. Previous research gives grounds to believe that groups of students that are more efficient than others use specific argument typologies in their interaction during the problem solving. Our project aims at collecting the interaction data and applying the existing approaches from computer science that allow to identify argument patterns.
Project LoRMAE   2013-2015
Logics for Reasoning about Multi-Agent Environments Multi-agent environments are inhabited by various ‘autonomous agents’. These are are entities capable of acting autonomously, i.e., without external intervention, in order to meet their design objectives. Although single-agent environments have been studied for a long time, studies about multi-agent environments are relatively new. This kind of environment brings new issues to be dealt with, such as non-deterministic actions, competition and cooperation among agents. Current solutions treat such issues by using some kind of multi-modal logic.
Projet Pajero (in french)   2011-2015
The Pajero project brings together industrial (Horizontal Software, EQUITIME, CAPS) and academic (CRIL, I3S, PRISM) partners and aims to implement a multiple resource management solution capable of handling large-scale problems using innovative constraint programming techniques and parallelism, particularly in the cloud. The scientific responsibility of the project lies with CRIL. This 4-year project (2011-2015) is financed by OSEO within the ISI program (Industrial Strategic Innovation). The budget allocated to the laboratory (more than 800 k€) has allowed the recruitment of 3 PhD students, 1 research engineer (3 years) and 1 post-doctoral student.
Projet PHC Barrande France / République Tchèque KC4CP   2017-2018
Knowledge compilation for constraint programming The principal aim of this joint project is to use the complementary strengths of both research teams to derive new results in the research area which lies on the borderline of knowledge compilation, SAT encoding and solving, and constraint programming. In this project, the focus will be laid on the CNF format, which is the main input format for existing solvers and compilers. Our objective is to identify and study a number of CNF fragments which can serve as useful output target languages for knowledge compilation.