动态调整注意力半径，让多智能体在资源有限时更高效协作。
DARRMS -- An Efficient Algorithm for Dynamic Attention Radius in Resource-Constrained Multi-Agent Systems
- 智能体自适应调整观察范围，减少计算负担。
- 实验证明该方法在资源受限下仍保持良好性能。
- 适合计算资源紧张的机器人与自动驾驶场景。
多智能体系统广泛应用于机器人、网络安全和自动驾驶等领域，但常受计算资源限制。传统决策框架假设完全可观测和无限算力，与现实不符。本文提出一种新算法，通过动态调整智能体的注意力半径，使其仅关注对决策必要的环境区域，从而降低计算需求。该方法同时优化注意力半径与决策过程，在不确定环境中提升了协调性与可扩展性。理论分析与实证结果表明，自适应观测能有效提升系统性能，维持鲁棒决策能力，适用于资源受限的多智能体系统。
原文摘要 · Abstract (English)
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.
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