Anticipation, prediction, and behavioral reliability in social interactions

Principal Investigators:

Jens Krause
Verena Hafner
Tim  Landgraf

Team members:

David Bierbach (postdoc)
Lea Musiolek (doctoral student)

 

Exploring prediction and anticipation in social interactions

Research Unit 1, SCIoI Project 10

Agents with predictive skills can anticipate future events and thus have a major advantage over agents that cannot (Pezzulo et al., 2008; Winfield and Hafner, 2018). In a social setting, the ability to anticipate an interaction partner’s future actions will allow to adaptively modulate own behavioral strategies (Moussaid et al., 2011). In a cooperative context, anticipation may help to enhance communication among agents while in a competitive context it gives room for manipulative strategies.

The proposed project will focus on the following four main objectives: In a first step, we will focus on developing analytical methods to identify heuristics of anticipation in live fish that interact with a biomimetic robotic fish (Robofish) and vary also in social responsiveness (objective a). In a second step, we will create situations in which live fish have to cooperate or compete with Robofish in order to achieve a goal effectively. This will allow us to estimate costs and benefits associated with anticipatory strategies (objective b). The aim of our experimental data is to identify cognitive heuristics that play a role in anticipation in interacting pairs that will then be used to develop a ubiquitous synthetic behavior of anticipation when social responsiveness in interaction partners varies (objective c). In order to evaluate this synthetic behavior, it will be implemented into Robofish as well as in humanoid robots and then tested in situations involving either robot-only pairs (robot-robot) or, in case of Robofish, also pairs with one live agent (fish-robot) (objective d).

 

 

Related Publications

Musiolek, L., Hafner, V. V., Krause, J., Landgraf, T., & Bierbach, D. (2020). Robofish as Social Partner for Live Guppies. In V. Vouloutsi, A. Mura, F. Tauber, T. Speck, T. J. Prescott, & P. F. M. J. Verschure (Eds.), Biomimetic and Biohybrid Systems (pp. 270–274). Springer. https://doi.org/10.1007/978-3-030-64313-3_26
Mellmann, H., Schlotter, B., Musiolek, L., & Hafner, V. V. (2020). Anticipation as a Mechanism for Complex Behavior in Artificial Agents. Artificial Life Conference Proceedings, 32, 157–159. https://doi.org/10.1162/isal_a_00314
Lukas, J., Kalinkat, G., Miesen, F. W., Landgraf, T., Krause, J., & Bierbach, D. (2021). Consistent Behavioral Syndrome Across Seasons in an Invasive Freshwater Fish. Frontiers in Ecology and Evolution, 8. https://doi.org/10.3389/fevo.2020.583670
Landgraf, T., Gebhardt, G. H. W., Bierbach, D., Romanczuk, P., Musiolek, L., Hafner, V. V., & Krause, J. (2020). Animal-in-the-Loop: Using Interactive Robotic Conspecifics to Study Social Behavior in Animal Groups. Annual Review of Control, Robotics, and Autonomous Systems. https://doi.org/10.1146/annurev-control-061920-103228
Landgraf, T., Moenck, H. J., Gebhardt, G. H. W., Weimar, N., Hocke, M., Maxeiner, M., Musiolek, L., Krause, J., & Bierbach, D. (2020). Socially competent robots: adaptation improves leadership performance in groups of live fish. ArXiv:2009.06633 [Cs]. http://arxiv.org/abs/2009.06633
Bierbach, D., Francisco, F., Lukas, J., Landgraf, T., Maxeiner, M., Romanczuk, P., Musiolek, L., Hafner, V. V., & Krause, J. (2021, July 19). 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
Bierbach, D., Wenchel, R., Gehrig, S., Wersing, S., O’Connor, O. L., & Krause, J. (2021). Male Sexual Preference for Female Swimming Activity in the Guppy (Poecilia reticulata). Biology, 10(2). https://doi.org/10.3390/biology10020147
Bierbach, D., Mönck, H. J., Lukas, J., Habedank, M., Romanczuk, P., Landgraf, T., & Krause, J. (2020). Guppies Prefer to Follow Large (Robot) Leaders Irrespective of Own Size. Frontiers in Bioengineering and Biotechnology, 8. https://doi.org/10.3389/fbioe.2020.00441

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