| On 2022-10-19 at 15:00:00 (Brussels Time) |
Abstract
We study how robot swarms can achieve a consensus on the best among a set of n possible options available in the environment. While the robots rely on local communication with one another, follow simple rules, and make estimates of the option’s qualities subject to measurement errors, the swarm as a whole is able to make accurate collective decisions. We compare the performance of two prominent decision-making algorithms that are based, respectively, on the direct-switching and the cross-inhibition models, in presence of self-sourcing information. Through robot-swarm simulations, we show that swarms programmed with the direct-switching model are only able to make consensus decisions in asymmetric environments where options have different quality values. Instead, using cross-inhibition, the robot swarm can also break decision deadlocks and reach a consensus in symmetric environments with equal quality options. We investigate the mechanistic causes of such differences and we find that the time the robots spend in a state of indecision is a key parameter to break the symmetry.
Link to Seminar:
https://teams.microsoft.com/l/meetup-join/19%3aYDVRTvS5v-FcnsTJisONwlDTQrEw-hclXJrmwzVxbkg1%40thread.tacv2/1665127596644?context=%7b%22Tid%22%3a%2230a5145e-75bd-4212-bb02-8ff9c0ea4ae9%22%2c%22Oid%22%3a%22dcfe88e1-1c78-4028-8f17-6357145735b2%22%7d
