DARRMS -- An Efficient Algorithm for Dynamic Attention Radius in Resource-Constrained Multi-Agent Systems
A new arXiv paper introduces DARRMS, an algorithm that reduces computational resource demands by allowing agents to limit their attention radius dynamically, improving coordination and scalability in uncertain environments while maintaining decision-making robustness.
[2606.12614] DARRMS -- An Efficient Algorithm for Dynamic Attention Radius in Resource-Constrained Multi-Agent Systems
[Submitted on 10 Jun 2026]
Title:DARRMS -- An Efficient Algorithm for Dynamic Attention Radius in Resource-Constrained Multi-Agent Systems
View a PDF of the paper titled DARRMS -- An Efficient Algorithm for Dynamic Attention Radius in Resource-Constrained Multi-Agent Systems, by Benjamin Alcorn and 1 other authors
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Abstract:Multi-agent systems are integral tools for various domains such as robotics, cybersecurity, and autonomous vehicle planning. These types of systems often have constraints on the computational resources, leading to a need for efficient lightweight algorithms. Traditional decision making frameworks often assume ideal conditions, such as full observability and unlimited computational capacity, which do not align with real-world challenges. In this paper, we introduce a new algorithm that allows for reduced demand on computational resources without a large cost of other performance metrics. Agents will limit their observability to some attention radius, which intentionally allows them to ignore parts of the environment that might be unnecessary for action planning. By optimizing both the attention radius and decision-making, our approach enhances coordination and scalability in uncertain environments. Through both theoretical analysis and empirical validation, we demonstrate the effectiveness of adaptive observation in improving system performance and maintaining robust decision-making strategies in resource-constrained systems.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.12614 [cs.RO]
(or arXiv:2606.12614v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.12614
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Benjamin Alcorn [view email] [v1] Wed, 10 Jun 2026 19:14:56 UTC (191 KB)
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