Noise-resistant and scalable collective preference learning via ranked voting in swarm robotics

Swarm robotics studies how to use large groups of cooperating robots to perform designated tasks. Given the need for scalability, individual members of the swarm usually have only limited sensory capabilities, which can be unreliable in noisy situations. One way to address this shortcoming is via collective decision-making, and utilizing peer-to-peer local interactions to enhance the behavioral performances of the whole swarm of intelligent agents. In this paper, we address a collective preference learning scenario, where agents seek to rank a series of given sites according to a preference order. We have proposed and tested a novel ranked voting-based strategy to perform the designated task. We use two variants of a belief fusion-based strategy as benchmarks. We compare the considered algorithms in terms of accuracy and precision of decisions as well as the convergence time. We have tested the considered algorithms in various noise levels, evidence rates, and swarm sizes. We have concluded that the proposed ranked voting approach is significantly cheaper and more accurate, at the cost of less precision and longer convergence time. It is especially advantageous compared to the benchmark when facing high noise or large swarm size.