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

TL;DR

This paper introduces a distributed greedy algorithm for dynamic task allocation in multi-agent systems, leveraging submodular optimization and $q$-independence systems to improve efficiency and flexibility.

Contribution

It develops a novel distributed greedy algorithm with approximation guarantees for submodular maximization under $q$-independence constraints, enhancing resource management in MASs.

Findings

DGBA outperforms benchmark algorithms in utility and resource efficiency.

DGBA maintains real-time computational feasibility in simulations.

The approach offers flexible modeling of heterogeneous resource constraints.

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.