DARRMS -- An Efficient Algorithm for Dynamic Attention Radius in Resource-Constrained Multi-Agent Systems

2026-06-10

AI Summary

This work addresses the challenge of inefficient coordinated decision-making in resource-constrained multi-agent systems, where limited computational capacity hinders performance. To tackle this issue, the authors propose a dynamic attention radius mechanism that adaptively adjusts each agent’s observation range, thereby reducing computational overhead while enhancing decision quality. By integrating a lightweight decision-making algorithm with an attention radius optimization strategy, the approach eliminates the reliance on full observability and high computational power inherent in conventional methods. Theoretical analysis and empirical evaluations demonstrate that the proposed framework effectively preserves system coordination, scalability, and decision robustness, all while achieving substantial reductions in resource consumption.

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.

Problem

• multi-agent systems
• resource-constrained
• attention radius
• decision-making
• observability

Innovation

• Dynamic Attention Radius
• Resource-Constrained Multi-Agent Systems
• Adaptive Observation
• Lightweight Algorithm
• Scalable Coordination