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

2026-05-22 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Advanced, extended

Summary

This overview examines goal-oriented communication in multi-agent systems (MAS), a paradigm shifting focus from message fidelity to information relevance for shared objectives. Traditional communication struggles with resource constraints like bandwidth and latency in autonomous systems, distributed control, and edge intelligence. The paper surveys foundational concepts, including Information Bottleneck (IB), Semantic Rate Distortion (SRD) theory, and G Theory, which quantify purposeful communication. It also explores learning-based approaches such as multi-agent reinforcement learning (MARL), sparsity, and attention mechanisms. Practical applications are highlighted across domains like swarm robotics, federated learning, edge computing, cooperative autonomous vehicles, and distributed SLAM. The analysis concludes by discussing open challenges, including the integration of information-theoretic and learning-based methods, scalability in large MAS, and ensuring the safety, reliability, and interpretability of emergent communication protocols.

Key takeaway

For Machine Learning Engineers and Robotics Engineers designing multi-agent systems, recognize that traditional communication protocols prioritizing raw data fidelity are inefficient under resource constraints. Instead, you should adopt goal-oriented communication paradigms that explicitly link message utility to task performance. Integrate information-theoretic models like Information Bottleneck or Semantic Rate Distortion, alongside learning-based approaches such as MARL with attention mechanisms, to enable agents to adaptively transmit only task-relevant information, significantly improving system efficiency and coordination.

Key insights

Goal-oriented communication optimizes MAS efficiency by prioritizing task-relevant information over raw data fidelity under resource constraints.

Principles

• Prioritize information based on task relevance, not raw fidelity.
• Balance data compression with preserving task-critical content.
• Message utility is defined by its contribution to collective goals.

Method

Goal-oriented communication involves optimizing message content and timing based on task-specific utility functions, often using information-theoretic principles or learned policies via MARL and attention mechanisms.

In practice

• Employ Information Bottleneck to learn task-relevant message encoding.
• Design communication policies using Semantic Rate Distortion for resource efficiency.
• Use attention mechanisms to prioritize messages from critical agents.

Topics

• Multi-Agent Systems
• Goal-Oriented Communication
• Semantic Communication
• Information Bottleneck
• Multi-Agent Reinforcement Learning
• Wireless Networked Control Systems

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.