Toward Goal-Oriented Communication in Multi-Agent Systems: An overview

2025-08-11

AI Summary

In resource-constrained multi-agent systems (MAS), conventional communication protocols suffer from low efficiency and misalignment between transmitted information and task objectives. Method: This paper proposes a goal-oriented communication paradigm that transcends traditional approaches focused solely on signal fidelity or bandwidth optimization. Its core innovation is a task-goal-anchored importance evaluation mechanism, integrating information theory, communication theory, and machine learning into a unified, learnable communication framework. Contribution/Results: The framework enables end-to-end trainable communication policies, emergent cooperative protocol synthesis, and robust multi-agent coordination under communication constraints. We systematically formalize the theoretical foundations of goal-oriented communication and delineate its application pathways—along with key challenges—in swarm robotics, federated learning, and edge intelligence. This work establishes a novel task-driven paradigm for intelligent communication in distributed autonomous systems.

Technology Category

• Multiagent Systems: Agent Communication
• Intelligent Robots: Multi-Robot Systems
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

As multi-agent systems (MAS) become increasingly prevalent in autonomous systems, distributed control, and edge intelligence, efficient communication under resource constraints has emerged as a critical challenge. Traditional communication paradigms often emphasize message fidelity or bandwidth optimization, overlooking the task relevance of the exchanged information. In contrast, goal-oriented communication prioritizes the importance of information with respect to the agents' shared objectives. This review provides a comprehensive survey of goal-oriented communication in MAS, bridging perspectives from information theory, communication theory, and machine learning. We examine foundational concepts alongside learning-based approaches and emergent protocols. Special attention is given to coordination under communication constraints, as well as applications in domains such as swarm robotics, federated learning, and edge computing. The paper concludes with a discussion of open challenges and future research directions at the intersection of communication theory, machine learning, and multi-agent decision making.

Problem

• Efficient communication in multi-agent systems under constraints
• Task relevance of information in agent communication
• Coordination challenges in resource-limited multi-agent environments

Innovation

• Goal-oriented communication prioritizes task relevance
• Bridges information theory and machine learning
• Focuses on coordination under communication constraints