DRAMA: A Dynamic and Robust Allocation-based Multi-Agent System for Changing Environments

2025-08-06

AI Summary

Existing multi-agent systems (MAS) predominantly adopt static architectures, featuring rigid task allocation and fixed agent capabilities, rendering them ill-suited for dynamic environments. To address this, we propose a dynamic MAS framework that decouples the control plane from the execution plane. Our approach introduces resource abstraction and object-oriented modeling to define a unified resource lifecycle for both agents and tasks. We design an affinity-based, loosely coupled task allocation mechanism enabling flexible runtime rescheduling. Furthermore, we integrate centralized planning with distributed execution to ensure continuous coordination amid dynamic agent addition and removal. The framework preserves robustness and scalability while significantly improving collaboration continuity and task execution efficiency. Empirical evaluation demonstrates its effectiveness in open, dynamic operational settings, establishing a scalable architectural paradigm for next-generation MAS.

Technology Category

• Multiagent Systems: Multiagent Planning
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Dynamic environments challenge static multi-agent systems
• Existing frameworks lack adaptability to changing conditions
• Need robust task allocation for unpredictable scenarios

Innovation

• Modular architecture with control and worker planes
• Affinity-based loosely coupled task allocation
• Real-time monitoring and centralized planning