Our recent publications


Veale, M., & Zuiderveen Borgesius, F. (2021). Demystifying the Draft EU Artificial Intelligence Act.

Riveiro, M. & Thill, S. (2021). "That's (not) the output I expected!": On the role of end user expectations in creating explanations of AI systems. Artificial Intelligence, 298:103507. doi: 10.1016/j.artint.2021.103507

Windridge, D., Svensson, H. & Thill, S. (2021). On the utility of dreaming: A general model for how learning in artificial agents can benefit from data hallucination. Adaptive Behavior, 29 (3), 267-280. doi: 10.1177/1059712319896489


Billing, E.A., Belpaeme, T., Cai, H., Cao, H.L., Ciocan, A., Costescu, C., David, D., Homewood, R.J., Hernandez Garcia, D., Gómez Esteban, P., Liu, H., Nair, V., Matu, S., Mazel, A., Selescu, M., Senft, E., Thill, S., Vanderborght, B., Vernon, D. & Ziemke, T. (2020). The DREAM Dataset: Supporting a data-driven study of autism spectrum disorder and robot enhanced therapy. PLoS One, 15 (8):e0236939. doi: 10.1371/journal.pone.0236939

Iacob, S.T., Kwisthout, J.H.P. & Thill, S. (2020). From models of cognition to robot control and back using spiking neural networks. In V. Vouloutsi, A. Mura, F. Tauber, T. Speck, T.J. Prescott & P.F.M.J. Verschure (Eds.), Biomimetic and Biohybrid Systems: Living Machines 2020 (pp. 176-191). Cham: Springer doi: 10.1007/978-3-030-64313-3_18

Zanatto, D., Patacchiola, M., Goslin, J., Thill, S. & Cangelosi, A. (2020). Do humans imitate robots? An investigation of strategic social learning in human-robot interaction. In HRI 2020: Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (pp. 449-457). Cambridge: Association for Computing Machinery (ACM) doi: 10.1145/3319502.3374776


Cao, H.L., Esteban, P.G., Bartlett, M.E., Baxter, P.E., Belpaeme, T., Billing, E.A., . . . Thill, S., Zhou, X. & Ziemke, T. (2019). Robot-enhanced therapy: Development and validation of supervised autonomous robotic system for autism spectrum disorders therapy. IEEE Robotics and Automation Magazine, 26 (2), 49-58. doi: 10.1109/MRA.2019.2904121


Carlo Baldassi, Federica Gerace, Hilbert J. Kappen, Carlo Lucibello, Luca Saglietti, Enzo Tartaglione, and Riccardo Zecchina (2018), Role of Synaptic Stochasticity in Training Low-Precision Neural NetworksPhys. Rev. Lett. 120

Lessmann, B. van Ginneken, M. Zreik, P.A. de Jong, B.D. de Vos, M.A. Viergever and I. Išgum (2018), Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions, IEEE Transactions on Medical Imaging;37(2):615-625.


Lopopolo, A., Frank, S., Van den Bosch, A., and Willems, R. (2017),Using stochastic language models (SLM) to map lexical, syntactic, and phonological information processing in the brain. PLoS ONE, 12:5: e0177794.


Güçlü, U, van Gerven, MAJ (2015). Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. J. Neurosci., 35(27):10005-10014.

Tom Claassen and Tom Heskes (2012). A Bayesian approach to constraint based causal inference. In UAI 2012, Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, pages 207-216, 2012. (Best paper award)

Bosse, T., Jonker, C.M., Meij, L. van der, Sharpanskykh, A., and Treur, J. (2009). Specification and Verification of Dynamics in Agent Models. International Journal of Cooperative Information Systems, vol. 18, pp. 167-193.