Stable Belief Estimation in Shepherd-Assisted Swarm Collective Decision Making

Abstract

Swarm collective decision making refers to the case where a swarm needs to make a decision based on different pieces of evidence collected by its individuals. This problem has been investigated by several recent studies which proposed strategies to enable the swarm to perform fast and accurate collective decision making. However, the performance of these strategies (in terms of its accuracy, speed and level of consensus) suffers significantly in complex environments. The aim of our work is to propose a collective decision-making strategy that promises a consistent performance across different levels of scenario complexity and achieves superiority over the existing strategies in highly complex scenarios. To achieve this aim, our proposed algorithm employs a shepherding agent to boost the performance of the swarm. The swarm members are only responsible for sensing the state of a feature distributed in the environment. Only the shepherd needs to be able to process position and navigation abilities to collect the swarm. The algorithm consists of two phases: exploration and belief sharing. In the exploration phase, swarm members navigate through an environment and sense its features. Then, in the belief sharing phase, a shepherding agent collects the swarm members together so that they can share their estimates and calculate their decisions. The results demonstrate that the proposed shepherding algorithm succeeds across different levels of scenario complexity. Additionally, the approach achieves high levels of accuracy and consensus in complex non-homogeneous environments where the baseline state-of-the-art algorithm fails.