Some recent publications

[J72] A. Ligot, A.Cotorruelo, E. Garone, and M. Birattari (2022).   Towards an empirical practice in off-line fully-automatic design of robot swarms.   IEEE Transactions on Evolutionary Computation, early access: 10.1109/TEVC.2022.3144848

[J71] J. Kuckling, V. van Pelt, and M. Birattari (2022)   AutoMoDe-Cedrata: Automatic design of behavior trees for controlling a swarm of robots with communication capabilities.   SN Computer Science, 3:136

[J70] D. Bozhinoski and M. Birattari (2021).   Towards an integrated automatic design process for robot swarms.   Open Research Europe, 1:112

[J69] K. Hasselmann, A. Ligot, J. Ruddick, and M.Birattari (2021).   Empirical assessment and comparison of neuro-evolutionary methods for the automatic off-line design of robot swarms.   Nature Communications, 12:4345

[C17] M. Birattari, A. Ligot, and G. Francesca (2021). AutoMoDe: a modular approach to the automatic off-line design and fine-tuning of control software for robot swarms. [PDF]   In N. Pillay and R. Qu (Eds.) Automated Design of Machine Learning and Search Algorithms, pp. 73-90. Springer Nature, Cham, Switzerland.

[J68] F. Pagnozzi and M. Birattari (2021).   Off-policy evaluation of the performance of a robot swarm: Importance sampling to assess potential modifications to the finite-state machine that controls the robots.   Frontiers in Robotics and AI, 8:625125

[J67] M. Kegeleirs, G. Grisetti, and M. Birattari (2021).   Swarm SLAM: challenges and perspectives.   Frontiers in Robotics and AI, 8:618268

Last update: Feb 1, 2022