Distributed Task Allocation for Multi-Agent Systems: A Submodular Optimization Approach
For multi-agent systems with limited communication and resources, this work provides a practical algorithm with theoretical guarantees, though it is an incremental improvement over existing matroid-based approaches.
The paper develops a distributed greedy bundles algorithm (DGBA) for dynamic task allocation in multi-agent systems with heterogeneous resource constraints, achieving higher total utility, resource efficiency, and assignment stability than benchmarks in micro-satellite simulations.
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.