Collective decision with 100 Kilobots: speed versus accuracy in binary discrimination problems

Gabriele Valentini (1, 2) , Eliseo Ferrante (3) , Heiko Hamann (2) , Marco Dorigo (1, 2)

1 ULB - Université libre de Bruxelles
2 University of Paderborn
3 KU Leuven - Catholic University of Leuven = Katholieke Universiteit Leuven

Achieving fast and accurate collective decisions with a large number of simple agents without relying on a central planning unit or on global communication is essential for developing complex collective behaviors. In this paper, we investigate the speed versus accuracy trade-off in collective decision-making in the context of a binary discrimination problem—i.e., how a swarm can collectively determine the best of two options. We describe a novel, fully distributed collective decision-making strategy that only requires agents with minimal capabilities and is faster than previous approaches. We evaluate our strategy experimentally, using a swarm of 100 Kilobots, and we study it theoretically, using both continuum and finite-size models. We find that the main factor affecting the speed versus accuracy trade-off of our strategy is the agents’ neighborhood size—i.e., the number of agents with whom the current opinion of each agent is shared. The proposed strategy and the associated theoretical framework can be used to design swarms that take collective decisions at a given level of speed and/or accuracy.

Domaines: Intelligence artificielle [cs.AI]; Complexité [cs.CC]; Apprentissage [cs.LG]; Système multi-agents [cs.MA]; Robotique [cs.RO]; Adaptation et Systèmes auto-organisés [nlin.AO]

Gabriele Valentini, Eliseo Ferrante, Heiko Hamann, Marco Dorigo. Collective decision with 100 Kilobots: speed versus accuracy in binary discrimination problems. Autonomous Agents and Multi-Agent Systems, 2016, 30 (3), pp.553-580. ⟨10.1007/s10458-015-9323-3⟩. ⟨hal-01403730⟩