Big data and artificial intelligence

NADI offers a broad expertise in artificial intelligence: bio-inspired robotics, robust, interactive, interpretable and safe machine learning, automatic program verification, declarative programming, business intelligence, knowledge representation and automatic software testing. This already lead to many collaborations with medical experts, industries and civil society. Together with other areas of expertise in NADI, experts in AI also explore educational, ethical, societal and legal implications of AI.



Artificial intelligence is a perfect illustration of the need of multidisciplinary research which is promoted by NADI. Indeed, since 1943, this field of computer science has always been interdisciplinary, drawing upon the expertise of other disciplines (for instance, neurosciences, psychology, philosophy, and mathematics) and solving problems in many applications domains (for instance, in medicine, human resources and industry) with ethical, societal and legal implications. 

NADI holds a large expertise in AI, e.g. in bio-inspired robotics, automatic code verification, business intelligence, knowledge representation, declarative programming and testing AI algorithms.  To cite a few key expertise, Elio Tuci aims to allow bio-inspired robots to operate in a complex environment and to learn from their experience in an autonomous way.  He designs control mechanisms underpinning complex behavioural, social, cognitive and communication capabilities and studies operational principles of cognition and learning in natural organisms.  Benoît Frénay aims to make machine learning techniques more robust, interactive, interpretable and safe to use.  Indeed, machine learning is now a widespread approach to solve many data intensive problems, but existing tools are sensitive to anomalies, hard to understand, hard to control and provide insufficient guarantees on their behaviour.  He is also working with colleagues in NADI about AI in education and AI legislation.  Wim Vanhoof develops techniques that allow to automatically verify whether a given software implements a particular algorithm.  These techniques have several applications, ranging from program comprehension over plagiarism detection and malware detection to advanced analyses and optimizations such as the automatic detection of parallelization strategies.  Isabelle Linden explores how business intelligence platforms can be developed to extract useful information from organisations.  She also develops new ways to model, acquire and manipulate weakly structured data such as texts and expert knowledge.  Jean-Marie Jacquet designs new programming methodologies to conceive programs by declaring what has to be solved, and not how to solve the problem at hand.  This line of research has been supported by Walloon Region projects to produce e.g. ExpeSurf, an expert system in multi-layer engineering, and Seplans, an expert system in estate planning.  He is also interested in the design of complex knowledge representation systems to model socio-technological systems.  Gilles Perrouin studies how software testing techniques can be applied to AI algorithms to make them safer to use.  He is also interested in the applications of AI to complex software systems.


Key publications

  • Narayan A., Tuci E., Labrosse F., Alkilabi M.H.M., A Dynamic Colour Perception System for Autonomous Robot Navigating on Unmarked Roads, Neurocomputing (Elsevier), Vol. 275, pp. 2251-2263, 20180.
  • Tuci E., Rabérin A., On the Design of Generalist Strategies for Swarms of Simulated Robots Engaged in Task-allocation Scenarios, Swarm Intelligence Journal (Springer), Volume 9, Issue 4, pp. 267-290, 2015.
  • Mesnard, E Payet, and W. Vanhoof, Towards a framework for algorithm recognition in binary code. In Principles and Practice of Declarative Programming, 2016. ACM Press.
  • Dandois and W. Vanhoof, Semantic code clones in logic programs. International Symposium on Logic-based Program Analysis and Transformation, LNCS, Springer 2012.
  • Colot, C., Linden, I., and Baecke, P. (2016). A Survey on Mobile Data Uses. International Journal of Decision Support System Technology, 8(2), 29-49. [3].
  • Gianni, M., Gotzamani, K., and Linden, I. (2016). How a BI-wise responsible integrated management system may support food traceability. International Journal of Decision Support System Technology, 8(2), 1-17. [1].
  • Jean-Marie Jacquet, Isabelle Linden, and Mihail-Octavian Staicu. Blackboard Rules: from a Declarative Reading to its Application for Coordinating Context-aware Applications in Mobile Ad Hoc Networks. Science of Computer Programming, 115-116: 79-99 (2016).
  • Jean-Marie Jacquet and Isabelle Linden. Fully Abstract Models and Refinements as Tools to Compare Agents in Timed Coordination Languages. Theoretical Computer Science, 410(2-3): 221-253 (2009).
  • Gilles Perrouin, Mathieu Acher, Maxime Cordy, Xavier Devroey: Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis, MASES@ASE 2018, Montpellier, France, September 3, 2018. ACM 2018
  • Christopher Henard, Mike Papadakis, Gilles Perrouin, Jacques Klein, Patrick Heymans, and Yves Le Traon. Bypassing the Combinatorial Explosion: Using Similarity to Generate and Prioritize T-Wise Test Configurations for Software Product Lines. IEEE Trans. Softw. Eng. 40, 7 (July 2014), 650-670. DOI:
  • Frénay, B., Verleysen, M. Classification in the Presence of Label Noise: a Survey. IEEE Trans. Neural Networks and Learning Systems, 25(5), 2014, p. 845-869.
  • Bibal, A. and Frenay, B. Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment. 2016 NIPS Workshop on Interpretable Machine Learning in Complex Systems. Barcelona


Key projects

  • EOS VeriLearn : Verifying Learning Artificial Intelligence Systems (2017 - …)
  • BEM : Business Event Manager, Walloon project on workflow reconstructions (2010-13)
  • SEPLANS : Expert System in Estate Planning, Walloon project on estate planning with AI, (2007-15).
  • Thesis of Christian Colot: the value of mobile data and customer relationship management.
  • EFFaTA-MEM : Evocative Framework for Text Analysis - MEdiality Models (2017 - …)


Contact: Benoît Frénay