研究资源分配：少而强的智能体比多而弱的更优吗？
Split over $n$ resource sharing problem: Are fewer capable agents better than many simpler ones?
- 将资源平均分给n个智能体，分析其覆盖效率随数量的变化
- 若智能体速度与面积成反比，单个智能体表现最佳
- 适合设计受限资源下的多智能体系统，如机器人协作
在多智能体系统中，有限资源应集中于少数能力强的智能体，还是分散给多个简单智能体？本文提出“n个智能体资源分配问题”，其中n个智能体均分一种公共资源（如预算、算力或物理尺寸）。以多智能体覆盖任务为例，智能体的圆形覆盖范围面积为1/n。理论分析表明，初始覆盖率随n增加而提升；若智能体速度与其半径成比例下降，则所有规模群体表现相当；但若速度与覆盖面积成比例下降，单一智能体表现最优。计算机模拟也显示，资源拆分会提高个体失败率。该模型与发现有助于识别最优资源分配程度，指导资源受限场景下的多智能体系统设计。
原文摘要 · Abstract (English)
In multi-agent systems, should limited resources be concentrated into a few capable agents or distributed among many simpler ones? This work formulates the split over $n$ resource sharing problem where a group of $n$ agents equally shares a common resource (e.g., monetary budget, computational resources, physical size). We present a case study in multi-agent coverage where the area of the disk-shaped footprint of agents scales as $1/n$. A formal analysis reveals that the initial coverage rate grows with $n$. However, if the speed of agents decreases proportionally with their radii, groups of all sizes perform equally well, whereas if it decreases proportionally with their footprints, a single agent performs best. We also present computer simulations in which resource splitting increases the failure rates of individual agents. The models and findings help identify optimal distributiveness levels and inform the design of multi-agent systems under resource constraints.
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