- The paper presents a unified analysis of coordination in multi-agent systems, emphasizing dependency management and collaborative learning approaches across diverse applications.
- It details key mechanisms such as centralized training, selective communication, and consensus achievement that facilitate effective coordination.
- The survey outlines future research directions addressing scalability, heterogeneous agent collaboration, and integration of large language models in MAS.
Multi-Agent Coordination across Diverse Applications: A Survey (2502.14743)
Introduction
"Multi-Agent Coordination across Diverse Applications: A Survey" provides a comprehensive analysis of multi-agent systems (MAS) and the coordination mechanisms facilitating their widespread use in various domains. This survey addresses four key questions: (1) What is coordination? (2) Why do we need coordination? (3) Who should be coordinated? (4) How to achieve coordination? By exploring these facets through a unified lens, the paper aims to bridge knowledge across applications and highlight current and future research directions.
Defining Multi-Agent Systems and Coordination
Multi-Agent Systems (MAS): Defined as systems composed of multiple independent agents that interact and make decisions, MAS embody problem solvers like robots, software agents, or distributed computing units. Each agent independently operates within the system, contributing to a collective goal while maintaining autonomy.
Coordination within MAS: Coordination involves managing dependencies among agents to optimize the system-level performance. It fundamentally answers "who to coordinate with" and "how to coordinate," determining agent interactions through social choices and dependency management. This process is crucial for system integration and amplifies the collective intelligence of MAS.
Framework for Multi-Agent Coordination
A framework for multi-agent coordination consists of:
- Evaluate System-Level Performance: It involves assessing the overall objective metrics to guide coordination.
- Social Choice: Who to Coordinate With: Identifying clusters or agents whose dependencies necessitate coordination. This includes determining inter-agent dependencies, coalition formation, and hierarchical versus decentralized structures in MAS.
- Implement Coordination: How to Coordinate: Mechanisms like centralized critic functions, parameter sharing, and attention mechanisms are key in facilitating coordinated learning. Coordination strategies involve task allocation, consensus achievement, and inter-agent learning interactions.
General Multi-Agent Tasks
The paper identifies several general coordination tasks critical to MAS applications:
- Coordinated Learning: Explores centralized training with decentralized execution paradigms, parameter sharing, and credit assignment mechanisms in multi-agent reinforcement learning, addressing learning dynamics, interaction dependencies, and efficiency.
- Communication and Cooperation: Discusses selective communication topologies, adaptive communication protocols, and message encoding, emphasizing strategies for efficient interaction and data exchange under resource constraints.
- Conflict-of-Interest Resolution: Focuses on resolving conflicts over shared resources through priority-based approaches, centralized solvers like conflict or dependency graphs, and distributed learning solutions leveraging communication protocols for enhanced coordination.
MAS Applications
The survey categorizes MAS applications into broad domains, detailing the nuances of coordination within each:
- Transportation Systems: Coordination in traffic signal controls and autonomous vehicles encompasses signal timing, intersection management, and vehicle platooning.
- Humanoid and Anthropomorphic Robots: Dual-arm robots, dexterous robot hands, and humanoid robots leverage coordination in grasp planning, motion synchronization, and sensory perception, enhancing adaptability and interaction.
- Satellite Systems: Satellite constellations and swarms optimize resources and services through coordinated planning, consensus, and distributed scheduling in communication architectures.
- LLM-Based MAS: LLMs exhibit collective intelligence in decision-making and behavior simulation tasks, such as collaborative programming, social networking, and game-playing, through LLM-enabled collaboration frameworks.
Future Directions and Challenges
The paper identifies future research avenues addressing key challenges in MAS:
- Scalability and Hybrid Coordination: Emphasizes the need for scalable coordination frameworks that combine hierarchical and decentralized approaches to manage complexity in large-scale MAS.
- Heterogeneity and Human-MAS Coordination: Highlights the role of specialized, heterogeneous agents, particularly human involvement, in MAS teamwork, introducing challenges in co-learning and trustworthy interactions.
- LLM-based MAS Learning Mechanisms: Calls for enhanced generalization and cost-efficient training approaches to improve LLM-based MAS applications.
Conclusion
This survey underscores the pivotal role of multi-agent coordination in advancing the efficacy of diverse applications. By addressing scalability, heterogeneity, and the integration of learning mechanisms such as LLMs, the paper anticipates significant developments in MAS coordination frameworks, driving a new phase of AI innovation.