Multi-agent systems (MAS) have demonstrated significant effectiveness in addressing complex problems through coordinated collaboration among heterogeneous agents. However, real-world environments and task specifications are inherently dynamic, characterized by frequent changes, uncertainty, and variability. Despite this, most existing MAS frameworks rely on static architectures with fixed agent capabilities and rigid task allocation strategies, which greatly limits their adaptability to evolving conditions. This inflexibility poses substantial challenges for sustaining robust and efficient multi-agent cooperation in dynamic and unpredictable scenarios. To address these limitations, we propose DRAMA: a Dynamic and Robust Allocation-based Multi-Agent System designed to facilitate resilient collaboration in rapidly changing environments. DRAMA features a modular architecture with a clear separation between the control plane and the worker plane. Both agents and tasks are abstracted as resource objects with well-defined lifecycles, while task allocation is achieved via an affinity-based, loosely coupled mechanism. The control plane enables real-time monitoring and centralized planning, allowing flexible and efficient task reassignment as agents join, depart, or become unavailable, thereby ensuring continuous and robust task execution. The worker plane comprises a cluster of autonomous agents, each with local reasoning, task execution, the ability to collaborate, and the capability to take over unfinished tasks from other agents when needed.

翻译：多智能体系统通过异构智能体间的协同协作，在解决复杂问题方面展现出显著效力。然而，现实环境与任务规范具有固有的动态特性，表现为频繁变化、不确定性和变异性。尽管存在这些特性，现有大多数多智能体系统框架仍依赖固定智能体能力与刚性任务分配策略的静态架构，极大限制了其对演化条件的适应性。这种僵化性为在动态不可预测场景中维持稳健高效的多智能体协作带来了重大挑战。针对上述局限，我们提出DRAMA：一种面向快速变化环境的动态稳健分配式多智能体系统，旨在促进韧性协作。DRAMA采用模块化架构，清晰分离控制平面与工作平面。智能体与任务均被抽象为具有明确定义生命周期的资源对象，并通过基于亲和性的松散耦合机制实现任务分配。控制平面支持实时监控与集中式规划，可在智能体加入、离开或不可用时灵活高效地重新分配任务，从而确保持续稳健的任务执行。工作平面由自主智能体集群构成，每个智能体具备局部推理、任务执行、协作能力，并能在必要时接管其他智能体未完成的任务。