We propose a multi-agent system that enables groups of agents to collaborate and work autonomously to execute tasks. Groups can work in a decentralized manner and can adapt to dynamic changes in the environment. Groups of agents solve assigned tasks by exploring the solution space cooperatively based on the highest reward first. The tasks have a dependency structure associated with them. We rigorously evaluated the performance of the system and the individual group performance using centralized and decentralized control approaches for task distribution. Based on the results, the centralized approach is more efficient for systems with a less-dependent system $G_{18}$, while the decentralized approach performs better for systems with a highly-dependent system $G_{40}$. We also evaluated task allocation to groups that do not have interdependence. Our findings reveal that there was significantly less difference in the number of tasks allocated to each group in a less-dependent system than in a highly-dependent one. The experimental results showed that a large number of small-size cooperative groups of agents unequivocally improved the system's performance compared to a small number of large-size cooperative groups of agents. Therefore, it is essential to identify the optimal group size for a system to enhance its performance.

翻译：我们提出了一种多智能体系统，使智能体群组能够协作并自主工作以执行任务。群组能够以去中心化方式运作，并能适应环境中的动态变化。智能体群组通过基于最高奖励优先的协作式探索解空间来解决分配的任务。这些任务具有关联的依赖结构。我们使用集中式和去中心化控制方法进行任务分配，严格评估了系统的整体性能以及各群组的个体表现。结果表明，对于依赖程度较低的系统 $G_{18}$，集中式方法效率更高；而对于依赖程度较高的系统 $G_{40}$，去中心化方法表现更优。我们还评估了向无相互依赖关系的群组分配任务的情况。研究发现，在低依赖系统中，分配给各群组的任务数量差异显著小于高依赖系统。实验结果显示，与少数大规模协作群组相比，大量小规模协作群组能够明确提升系统性能。因此，确定系统的最优群组规模以增强其性能至关重要。