Seminar: Tool use and agency in artificial agents

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Verena Hafner, Humboldt University Berlin

Tool use and agency in artificial agents
Sense of agency and sense of body ownership are important prerequisites for a minimal self in humans. When we look into the prerequisites for such a minimal self in artificial agents, a computational sense of agency can be created by predictive internal models that are able to predict the sensory consequences of a particular motor action of the agent based on previous experience (Hafner et al., 2020). The predictive models need to be adapted to the current context of the agent and its environment. Here, we will present a variety of models (Ciria et al., 2021) and discuss how they can be used for decision making and tool-use.
Hafner, V.V., Loviken, P., Pico Villalpando, A., Schillaci, G. (2020). Prerequisites for an Artificial Self. Frontiers in Neurorobotics. Vol. 14, p.5. doi:10.3389/fnbot.2020.00005. ISSN 1662-5218. https://www.frontiersin.org/articles/10.3389/fnbot.2020.00005/full
Ciria, A., Schillaci, G., Pezzulo, G., Hafner, V.V. & Lara, B. (2021). Predictive Processing in Cognitive Robotics: a Review. Neural Computation (2021) 33 (5): 1402–1432. https://doi.org/10.1162/neco_a_01383

Verena Hafner is Professor of Adaptive Systems at Humboldt-Universität zu Berlin and Head of the Adaptive Systems Group at the Department of Computer Science. She holds a Master with distinction in Computer Science and AI from the University of Sussex, UK, and a PhD from the Artificial Intelligence Lab, University of Zurich, Switzerland. Before moving to Berlin, she worked as an associate researcher in the Developmental Robotics Group at Sony Computer Science Labs in Paris, France. She is part of the Programme Committee of the DFG Priority Programme The Active Self (SPP 2134), PI in the DFG Cluster of Excellence “Science of Intelligence”, PI in the EU project METATOOL (2022-2026) and PI in the EU H2020 project HIVEOPOLIS (2019-2024). She is reviewing for the EU, BMBF, DFG, AvH, journals and conferences. Her research interests include sensorimotor interaction and learning, joint attention, internal models, and exploration strategies.

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