Fritz Francisco

Doctoral researcher
mail: fritz.francisco@hu-berlin.de

An avid biologist by training, Fritz has previously worked on various aquatic animal systems, both in the field and in controlled laboratory environments, to better understand the mechanisms governing social interactions. Now he has joined the interdisciplinary cluster Science of Intelligence as part of the project on Dynamical Collective Adaptation & Learning to elucidate on the fact that animals are capable of continuously adapting to changing environments and novel situations.

One way individuals are capable of doing so is by learning, a form of  inter- individual information transfer and knowledge accumulation. It enables an individual to understand its environment and  therefore minimize uncertainty about future situations. Using schooling fish (whose social and physical context as well as the previous experience and knowledge of each individual can easily be manipulated,) this project addresses the topic of adaptation and learning in animal groups. These insights can then be applied to approaches of artificial intelligence, in order to make these more robust in natural environments and under dynamic circumstances.

At SCIoI, Fritz is working on Project 11.

SCIoI Publications:

Ehlman, S., Scherer, U., Bierbach, D., Francisco, F., Laskowski, K., Krause, J., & Wolf, M. (2023). Leverging big data to uncover the eco-evolutionary factors shaping behavioural development. Proceedings of the Royal Society B. https://doi.org/10.1098/rspb.2022.2115
Bierbach, D., Gómez-Nava, L., Francisco, F. A., Lukas, J., Musiolek, L., Hafner, V. V., Landgraf, T., Romanczuk, P., & Krause, J. (2022). Live fish learn to anticipate the movement of a fish-like robot. Bioinspiration & Biomimetics. https://doi.org/10.1088/1748-3190/ac8e3e
Bierbach, D., Francisco, F., Lukas, J., Landgraf, T., Maxeiner, M., Romanczuk, P., Musiolek, L., Hafner, V. V., & Krause, J. (2021). Biomimetic robots promote the 3Rs Principle in animal testing. ALIFE 2021: The 2021 Conference on Artificial Life. https://doi.org/10.1162/isal_a_00375

 

 

Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) im Rahmen der Exzellenzstrategie des Bundes und der Länder – EXC-2002/1 – Projektnummer 390523135 .
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany ́s Excellence Strategy – EXC-2002/1 – Projectnumber 390523135.

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