On the evolution of mechanisms for three-option collective decision-making in a swarm of simulated robots

To act cohesively as a group, robot swarms must be able to make decisions collectively. Collective decision-making refers to a process in which once a group decision is reached, it cannot be attributed to any single individual. Although extensive research has been conducted in swarm robotics using hand-coded design techniques to develop individual mechanisms for collective decision-making, the proposed mechanisms are generally limited in terms of robustness, scalability, and adaptability. In this paper, we employ evolutionary computation techniques to synthesise neural network-based decision-modules underpinning the individual opinion selection in robots. We describe the group dynamics underlying the decision process that leads to consensus in a three-option perceptual discrimination task. We test the robustness, scalability and adaptability of the decision-module in a variety of conditions. We show that the decision-making mechanisms underpinned by the evolved decision-module are more effective in supporting the collective decision-making process than the hand-coded voter and majority models, both in terms of accuracy and with respect to time to convergence to consensus.