I am a directeur de recherches (research director) of the fund for scientific research F.R.S.–FNRS of Belgium's Wallonia-Brussels Federation. I am affiliated with IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium.
I have been awarded an ERC Consolidator Grant by the European Research Council for the project "DEMIURGE: automatic design of robot swarms".
My research focuses mainly on computational intelligence and in particular on the automatic design of swarm intelligence systems and multi-robot systems. My research interests include also metaheuristics and local search methods for combinatorial optimization. I am particularly interested in the application of statistical methods, design of experiments, and machine learning techniques, notably for assessing the performance of algorithms and for fine-tuning their parameters. I am also interested in epistemology and in the philosophical foundations of computational intelligence.
I am an associate editor for Frontiers in Robotics and AI: Multi-Robot Systems, and an academic editor for PeerJ Computer Science. I served as an associate editor for Swarm Intelligence from 2006 to 2019 and I am currently a member of the editorial board. I also served as a member of the organizing committee for 17 international conferences, and as a member of the program committee for more than 50 international conferences in the last five years. From 2002 to 2018, I served as member of the organizing committee of the series of conferences ANTS. Between 2012 and 2015, I chaired for four times the AAAI Video Competition.
Nature Machine Intelligence
About this article
In this Perspective, we contend that two cases should be disentangled in the optimization-based design of robot swarms: semi-automatic design, in which a human designer operates and steers an optimisation process; and (fully) automatic design, in which the optimisation process does not involve, need, or allow any human intervention.
This work is a core conceptual contribution of the ERC project DEMIURGE.
in a robot swarm
Volume 3, Issue 20, eaat0430
About this article
A study published in Science Robotics shows that a swarm of robots can collectively determine the correct order in which some given tasks must be executed, even if the individual robots comprised in the swarm are unable to do it alone.
In a robot swarm, the ability to collectively sequence tasks, which is an albeit simple form of planning, can emerge from the interaction of reactive individual robots.
The research was conducted in the context of the ERC project DEMIURGE.
Science covered our work. See Shandria Sutton's "Simple robots form a chain gang to solve complex problems", doi:10.1126/science.aau8870
See also the video they produced:
ERC Consolidator Grant
The research project "DEMIURGE: automatic design of robot swarms" has been selected for funding by the European Research Council via an ERC Consolidator Grant — principal investigator: Mauro Birattari
The scope of the project is the automatic design of robot swarms. Swarm robotics is an appealing approach to the coordination of large groups of robots. Up to now, robot swarms have been designed via some labor-intensive process. My goal is to advance the state of the art in swarm robotics by developing the DEMIURGE: an intelligent system that is able to design and realize robot swarms in a totally integrated and automatic way.
Lecture on the automatic modular design of robot swarms
In this lecture, I sketch the fundamental concepts of the automatic modular design of collective behaviors for robot swarms and I presents my research.
Course: Swarm Intelligence (INFO-H-414), Université libre de Bruxelles. Acedemic year: 2020-2021.
Swarmanoid the movie
The video "Swarmanoid, the movie" won the Best Video Award at the AAAI-11 AI Video Competition; the Innovative Technology Award at the Robot Film Festival; and the Prix Wernaers 2012 pour la recherche et la diffusion des connaissances.
This video, written and directed by myself and Rehan O'Grady, presents the main results of the Swarmanoid project, a FET-OPEN project funded by the European Commission and coordinated by Marco Dorigo. For more information, see the website of the Swarmanoid project.
About this book
The importance of tuning metaheuristics is widely acknowledged. However, there is very little dedicated research on the subject. Typically, scientists and practitioners tune metaheuristics by hand, guided only by their experience and by some rules of thumb. Tuning metaheuristics is often considered to be more of an art than a science.
This book lays the foundations for a scientific approach to tuning metaheuristics. The fundamental intuition that underlies Birattari's approach is that the tuning problem has much in common with the problems that are typically faced in machine learning. By adopting a machine learning perspective, the author gives a formal definition of the tuning problem, develops a generic algorithm for tuning metaheuristics, and defines an appropriate experimental methodology for assessing the performance of metaheuristics.