Distributed Task Allocation for Multi-Agent Systems: A Submodular Optimization Approach
Abstract
This paper addresses dynamic task allocation in resource-constrained multi-agent systems (MASs) with sequentially updated assignments. We develop a submodular maximization framework integrated with -independence systems, demonstrating greater flexibility than conventional matroid-based constraints for modeling heterogeneous resource limitations. The proposed distributed greedy bundles algorithm (DGBA) addresses communication limitations in MASs while providing rigorous approximation guarantees for submodular maximization under a -independence system constraint, ensuring low computational complexity. DGBA achieves feasible task allocation in polynomial time with reduced space complexity compared to existing methods. Extensive Monte Carlo simulations in a micro-satellite observation scenario demonstrate that DGBA consistently outperforms benchmark algorithms in total utility, resource efficiency, and assignment stability, while maintaining real-time computational feasibility.
Cite
@article{arxiv.2412.02146,
title = {Distributed Task Allocation for Multi-Agent Systems: A Submodular Optimization Approach},
author = {Jing Liu and Fangfei Li and Xin Jin and Yang Tang},
journal= {arXiv preprint arXiv:2412.02146},
year = {2026}
}