DRAMA：面向动态环境中弹性多智能体生态的下一代动态编排
DRAMA: Next-Gen Dynamic Orchestration for Resilient Multi-Agent Ecosystems in Flux
DRAMA 的多层架构与事件触发重调度机制可被视觉研究者用于设计动态多机器人协作系统，其集体空间智能模块整合分布式观测进行预测推理，为多视角视觉感知与决策融合提供可复用框架。
原文依据 DRAMA adopts a multilayer architecture that incorporates three principal mechanisms: adaptive scheduling through an affinity-driven mechanism, fault-tolerant continuity via hierarchical trust-chain task takeover, and collective spatial intelligence that consolidates distributed observations for predictive reasoning.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论具身多智能体系统通过异构智能体间的协同合作，已被证明能高效解决复杂任务。然而，现实环境与任务规范本质上是动态的，频繁变化、不确定性与变异性并存。尽管存在这些特性，现有大多数框架仍采用静态架构，智能体能力固定且任务分配策略僵化，这严重制约了它们对不断变化条件的适应能力。这种不灵活性为在动态且不可预测的环境中维持鲁棒高效的多智能体协作带来了重大挑战。为解决这些局限，我们提出 DRAMA，全称为面向弹性多智能体生态的动态编排，专为快速变化的环境量身定制。DRAMA 采用多层架构，包含三个主要机制：通过亲和力驱动机制实现自适应调度，通过分层信任链任务接管实现容错连续性，以及通过整合分布式观测进行预测推理的集体空间智能。这些组件共同实现了事件触发的重调度与去中心化故障恢复，确保在智能体到达、退出或恢复期间任务执行不中断。在具身 VirtualHome-Social 环境中的大量实验表明，与最优基线相比，DRAMA 在运行时效率上提升了 7%，吞吐量增加了 10%，同时在动态智能体种群下保持了优越的稳定性与鲁棒性。
英文原文摘要 1622 字符
Embodied Multi-Agent Systems have proven highly effective in addressing complex tasks through coordinated collaboration among heterogeneous agents. However, real-world environments and task specifications are inherently dynamic, exhibiting frequent changes, uncertainty, and variability. Despite these characteristics, most existing frameworks employ static architectures with fixed agent capabilities and rigid task allocation strategies, which substantially constrain their adaptability to evolving conditions. This inflexibility presents significant challenges to maintaining robust and efficient multi-agent cooperation in dynamic and unpredictable settings.To address these limitations, we propose DRAMA, short for Dynamic Orchestration for Resilient Multi-Agent Ecosystems, tailored for rapidly changing environments. DRAMA adopts a multilayer architecture that incorporates three principal mechanisms: adaptive scheduling through an affinity-driven mechanism, fault-tolerant continuity via hierarchical trust-chain task takeover, and collective spatial intelligence that consolidates distributed observations for predictive reasoning. Together, these components enable event-triggered rescheduling and decentralized fault recovery, ensuring uninterrupted task execution amid agent arrivals, dropouts, or recoveries. Extensive experiments in the embodied VirtualHome-Social environment demonstrate that DRAMA achieves a 7% improvement in runtime efficiency and a 10% increase in throughput compared with state-of-the-art baselines, while maintaining superior stability and robustness under dynamic agent populations.