J. Kuckling, "Recent trends in robot learning and evolution for swarm robotics". Frontiers in Robotics and AI, vol. 10, p. 1134841, 2023, DOI: 10.3389/frobt.2019.00059.

J. Kuckling, V. van Pelt, and M. Birattari, “AutoMoDe-Cedrata: Automatic design of behavior trees for controlling a swarm of robots with communication capabilities”. SN Computer Science, vol. 3, p. 136, 2022, DOI: 10.1007/s42979-021-00988-9.

A. Ligot*, J. Kuckling*, D. Bozhinoski, and M. Birattari, “Automatic modular design of robot swarms using behavior trees as a control architecture”. PeerJ Computer Science, vol. 6, p. e314, 2020, DOI: 10.7717/peerj-cs.314.

J. Kuckling, T. Stützle, and M. Birattari, “Iterative improvement in the automatic modular design of robot swarms”. PeerJ Computer Science, vol. 6, p. e322, 2020, DOI: 10.7717/peerj-cs.322.

M. Birattari, A. Ligot, D. Bozhinoski, M. Brambilla, G. Francesca, L.
Garattoni, D. Garzón Ramos, K. Hasselmann, M. Kegeleirs, J. Kuckling, F.
Pagnozzi, A. Roli, M. Salman, and T. Stützle, “Automatic off-line design of robot swarms: A manifesto”. Frontiers in Robotics and AI, vol. 6, p. 59, 2019, DOI: 10.3389/frobt.2019.00059.

J. Kuckling*, K. Ubeda Arriaza*, and M. Birattari, “AutoMoDe-IcePop: Automatic modular design of control software for robot swarms using simulated annealing”. In Artificial intelligence and machine learning: BNAIC 2019, BENELEARN 2019, vol. 1196, B. Bogaerts et al., Eds. Cham, Switzerland: Springer, 2020, pp. 3–17. DOI: 10.1007/978-3-030-65154-1_1.

P. Leopardi, H. Hamann, J. Kuckling, and T. K. Kaiser, “Density-driven emergent division of labor in evolutionary multi-robot systems”. In The 18th International Conference on the Simulation of Adaptive Behavior (SAB 2026).

P. Leopardi, H. Hamann, J. Kuckling, and T. K. Kaiser, “The evaluation cost of task specialization in evolutionary multi-robot systems”. In 2026 Genetic and Evolutionary Computation Conference (GECCO).

M. Kraus and J. Kuckling, “Generative adversarial imitation learning for robot swarms: learning from human demonstrations and trained policies”. In 2026 IEEE International Conference on Robotics and Automation (ICRA), 2026.

A. Fialho Jesus and J. Kuckling, “How swarms differ: challenges in collective behaviour comparison”. In Swarm intelligence: 15th international conference, ANTS 2026, 2026, vol. 16515, pp. 376–384. DOI: 10.1007/978-3-032-26123-6_32.

P. Leopardi, H. Hamann, J. Kuckling, and T. K. Kaiser, “On the cost of evolving task specialization in multi-robot systems”. In Swarm intelligence: 15th international conference, ANTS 2026, 2026, vol. 16515, pp. 402–410. DOI: 10.1007/978-3-032-26123-6_35.

Y. Wesseloh, P. Leopardi, J. Kuckling, and H. Hamann, “Minimizing uncertainty as a principle for task allocation in robot swarms”. In Swarm intelligence: 15th international conference, ANTS 2026, 2026, vol. 16515, pp. 494–495. DOI: 10.1007/978-3-032-26123-6.

G. Aguirre, S. Atasoy Bingöl, H. Hamann, and J. Kuckling, “Bayesian decentralized decision-making for multi-robot systems: sample-efficient estimation of event rates”. In 2025 IEEE International Symposium on Multi-Robot and Multi-Agent Systems (MRS), 2025, pp. 1–7. DOI: 10.1109/MRS66243.2025.11357254.

J. Kuckling, R. Luckey, V. Avrutin, A. Vardy, A. Reina, and H. Hamann, “Do we run large-scale multi-robot systems on the edge? More evidence for two-phase performance in system size scaling”. In 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024, pp. 4562–4568. DOI: 10.1109/ICRA57147.2024.10610771. Best multi-robot-systems paper award ICRA 2024.

 I. Gharbi, J. Kuckling, D. Garzón Ramos, and M. Birattari, "Show me what you want: inverse reinforcement learning to automatically design robot swarms by demonstration". In 2023
IEEE International Conference on Robotics and Automation (ICRA), 2023.

D. Garzón Ramos, D. Bozhinoski, G. Francesca, L. Garattoni, K. Hasselmann, M. Kegeleirs, J. Kuckling, A. Ligot, F. J. Mendiburu, F. Pagnozzi, M. Salman, T. Stützle, and M. Birattari, “The automatic off-line design of robot swarms: Recent advances and perspectives”. In R2T2: Robotics research for tomorrow’s technology, 2021.

J. Kuckling*, V. van Pelt*, and M. Birattari, “Automatic modular design of behavior trees for robot swarms with communication capabilities”. In Applications of evolutionary computation: 24th international conference, EvoApplications 2021, 2021, vol. 12694, pp. 130–145. DOI: 10.1007/978-3-030-72699-7_9.

J. Kuckling*, A. Ligot*, D. Bozhinoski, and M. Birattari, “Behavior trees as a control architecture in the automatic modular design of robot swarms”. In Swarm intelligence: 11th international conference, ANTS 2018, 2018, vol. 11172, pp. 30–43. DOI: 10.1007/978-3-030-00533-7_3.

J. Kuckling*, K. Ubeda Arriaza*, and M. Birattari, “Simulated annealing as an optimization algorithm in the automatic modular design of robot swarms”. In Proceedings of the reference AI & ML conference for belgium, netherlands & luxemburg, BNAIC/BENELEARN 2019, 2019, vol. 2491. Best paper award BNAIC 2019.

G. Legarda Herranz, D. Garzón Ramos, J. Kuckling, M. Kegeleirs, and M. Birattari, “Tycho:
a robust, ROS-based tracking system for robot swarms”. IRIDIA, Université Libre de Bruxelles, Brussels, Belgium, TR/IRIDIA/2022-009, 2022.

J. Kuckling, K. Hasselmann, V. van Pelt, C. Kiere, and M. Birattari, “AutoMoDe Editor: A visualization tool for AutoMoDe”. IRIDIA, Université Libre de Bruxelles, Brussels, Belgium, TR/IRIDIA/2021-009, 2021.

J. Kuckling, A. Ligot, D. Bozhinoski, and M. Birattari, “Search space for AutoMoDe-Chocolate and AutoMoDe-Maple”. IRIDIA, Université Libre de Bruxelles, Brussels, Belgium, TR/IRIDIA/2018-012, 2018.

K. Hasselmann, A. Ligot, G. Francesca, D. Garzón Ramos, M. Salman, J. Kuckling, F. J. Mendiburu, and M. Birattari, “Reference models for AutoMoDe”. IRIDIA, Université Libre de Bruxelles, Brussels, Belgium, TR/IRIDIA/2018-002, 2018.