# Distributed Task Allocation for Multi-Agent Systems: A Submodular Optimization Approach

arXiv: https://arxiv.org/abs/2412.02146 · HTML: https://mlsanity.com/paper/2412.02146/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [MAS-H2: A Hierarchical Multi-Agent System for Holistic Cloud-Native Autoscaling](https://mlsanity.com/paper/2603.07607/) (2026)

## Abstract (arXiv)

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 q-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 q-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.