Communication-Efficient Decentralized Task Allocation for Large-Scale Multi-Agent Systems

This letter presents a novel method to solve the decentralized task allocation problem for large-scale multi-agent systems (MASs), with an emphasis on communication efficiency. Conventional methods typically depend on sharing the localized task allocation plans of various agents across a communication network to facilitate a globally consistent task allocation plan for the entire MAS. However, communication capabilities are limited in most practical scenarios, which may sometimes fail to meet the demands of decentralized task allocation, especially when dealing with large numbers of agents and tasks. To address these challenges, a grouping performance impact (GPI) algorithm is proposed to minimize the total number of communications within the MAS while maintaining high task allocation performance. Agents are partitioned into various groups, with a leader agent utilized in each group to generate task allocation plans for the other follower agents. Additionally, a decentralized task allocation method for agent groups is proposed, incorporating a novel cost scheme designed to maximize the number of successfully performed tasks. Comprehensive simulations demonstrate that the proposed GPI algorithm outperforms the state-of-the-art decentralized methods by reducing communication overhead and improving the total number of successfully performed tasks.