| On 2023-10-16 at 14:00:00 (Brussels Time) |
Abstract
Decision-making in embodied agents should be fast and flexible if it is to successfully manage the uncertainty, variability, and dynamic environmental change encountered when operating in the real world. Decision-making is fast if it breaks indecision as quickly as indecision becomes costly. Decision-making is flexible if it adapts to signals important to successful operation, even if they are weak or infrequent.
I will present theory fundamentals, analytical results, and applications of a nonlinear dynamical model of excitable decision-making that allows analyzing and designing fast-and-flexible decision-making in embodied agents. The model is grounded on the principles of feedback control and bifurcation theory. Feedback control theory is key to capture the continuous regulation properties of fast-and-flexible decision-making. Bifurcation theory is key to capture the switch-like nature of decision-making events. The theory will be illustrated on simulations and applications in robotics, neuroscience, and neuromorphic engineering. Short Bio of the Speaker
Alessio Franci received his Laurea Specialistica Degree in Theoretical Physics from the University of Pisa in 2008 and his PhD in Physics and Control Theory from the University of Paris Sud 11 in 2012. Between 2012 and 2015 he was a postdoctoral researcher at the University of Liege and at INRIA Lille and a long-term visiting researcher at the University of Cambridge. Between 2015 and 2022 he was professor in the Math Department of the National Autonomous University of Mexico. Since 2023 he has been professor in the Department of Electrical Engineering and Computer Science of the University of Liege. His research is interdisciplinary but the central focus is on the control-theoretical and computational principles needed to understand and design biological and bio-inspired intelligent behaviors.
Link to Seminar:
https://teams.microsoft.com/l/meetup-join/19%3aYDVRTvS5v-FcnsTJisONwlDTQrEw-hclXJrmwzVxbkg1%40thread.tacv2/1694360930717?context=%7b%22Tid%22%3a%2230a5145e-75bd-4212-bb02-8ff9c0ea4ae9%22%2c%22Oid%22%3a%22dcfe88e1-1c78-4028-8f17-6357145735b2%22%7d
