A machine education approach to swarm decision-making in best-of-n problems

In swarm decision making, hand-crafting agents’ rules that use local information to achieve desirable swarm-level behaviours is a non-trivial design problem. Instead of relying entirely on swarm experts for designing these local rules, machine learning (ML) algorithms can be utilised for learning some of the local rules by mapping an agent’s perception to an appropriate action. To facilitate this process, we propose the use of Machine Education (ME) as a systematic approach for designing a curriculum for teaching the agents the required skills to autonomously select appropriate behaviours. We study the use of ME in the context of decision-making in best-of-n problems. The proposed approach draws on swarm robotics expertise for identifying agents’ capabilities and limitations, the skills required for generating the desirable behaviours, and the corresponding performance measures. In addition, ME utilises ML expertise for the selection and development of the ML algorithms suitable for each skill. The results of the experimental evaluations demonstrate the superior efficacy of the ME-based approach over the state-of-the-art approaches with respect to speed and accuracy. In addition, our approach shows considerable robustness to changes in swarm size and to changes in sensing and communication noise. Our findings promote the use of ME for teaching swarm members the required skills for achieving complex swarm tasks.