This paper investigates dynamic task allocation for multi-agent systems (MASs) under resource constraints, with a focus on maximizing the global utility of agents while ensuring a conflict-free allocation of targets. We present a more adaptable submodular maximization framework for the MAS task allocation under resource constraints. Our proposed distributed greedy bundles algorithm (DGBA) is specifically designed to address communication limitations in MASs and provides rigorous approximation guarantees for submodular maximization under $q$-independent systems, with low computational complexity. Specifically, DGBA can generate a feasible task allocation policy within polynomial time complexity, significantly reducing space complexity compared to existing methods. To demonstrate practical viability of our approach, we apply DGBA to the scenario of active observation information acquisition within a micro-satellite constellation, transforming the NP-hard task allocation problem into a tractable submodular maximization problem under a $q$-independent system constraint. Our method not only provides a specific performance bound but also surpasses benchmark algorithms in metrics such as utility, cost, communication time, and running time.

翻译：本文研究资源约束下的多智能体系统动态任务分配问题，重点在于最大化智能体全局效用，同时确保目标的无冲突分配。我们提出了一种更具适应性的次模最大化框架，用于解决资源约束下的多智能体系统任务分配问题。所提出的分布式贪婪捆绑算法专门针对多智能体系统中的通信限制设计，并为$q$独立系统下的次模最大化问题提供严格的近似保证，且具有较低的计算复杂度。具体而言，DGBA能在多项式时间复杂度内生成可行的任务分配策略，与现有方法相比显著降低了空间复杂度。为验证方法的实际可行性，我们将DGBA应用于微纳卫星星座主动观测信息获取场景，将NP难任务分配问题转化为$q$独立系统约束下可处理的次模最大化问题。该方法不仅提供了具体的性能界，还在效用、成本、通信时间和运行时间等指标上超越了基准算法。