Split over $n$ resource sharing problem: Are fewer capable agents better than many simpler ones?
Karthik Soma, Mohamed S. Talamali, Genki Miyauchi, Giovanni Beltrame, Heiko Hamann + 1 more
TLDR
This paper analyzes the "split over n resource sharing problem," revealing how resource distribution and agent speed scaling impact multi-agent system performance.
Key contributions
- Formulates the "split over n resource sharing problem" for multi-agent systems.
- Shows initial coverage rate grows with more agents ($n$) in a coverage case study.
- Finds performance equality if agent speed scales with radius, but a single agent is best if speed scales with footprint.
- Simulations indicate that resource splitting can increase individual agent failure rates.
Why it matters
This research provides a formal framework to understand the trade-offs in distributing resources among agents. Its findings offer crucial insights for designing efficient multi-agent systems, helping engineers decide between centralized and distributed architectures under various constraints.
Original Abstract
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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