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arXiv · 2412.02146

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

Jing Liu·Fangfei Li·Xin Jin·Yang Tang
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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 $q$-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.

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Jing Liu, Fangfei Li, Xin Jin, Yang Tang. 2024-12-03. Distributed Task Allocation for Multi-Agent Systems: A Submodular Optimization Approach. https://arxiv.org/abs/2412.02146

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