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.

翻译：多智能体系统是机器人学、网络安全和自动驾驶规划等领域的核心工具。此类系统通常受限于计算资源，需要高效的轻量级算法。传统决策框架往往假设理想条件（如完全可观测性与无限计算能力），这与现实挑战不符。本文提出了一种新算法，可在不显著牺牲其他性能指标的前提下降低计算资源需求。智能体将观测范围限制在特定注意力半径内，有意识地忽略环境中对行动规划可能无用的部分。通过联合优化注意力半径与决策过程，本方法在不确定环境中增强了系统的协调性与可扩展性。通过理论分析与实验验证，我们证明了自适应观测机制在资源受限系统中提升系统性能、维持鲁棒决策策略的有效性。