A Survey on Challenges and Emerging Frontiers of Multi-Agent Systems

Multi-Agent Systems (MAS) have emerged as a fundamental
approach for solving dynamic and distributed problems across domains
such as robotics, communication networks, and intelligent decision-making.
MAS agents are characterized by autonomy, sociality, and flexibility. Recent
advances such as deep reinforcement learning (DRL), large language
model (LLM)-based agents, and context awareness have broadened MAS
capabilities, but existing surveys are often limited to specific subdomains
or focus on outdated platforms, ignoring the convergence between
learning-based and language-based systems. This review provides a comprehensive,
technically rigorous view that connects classical MAS theory
with emerging paradigms. We analyze cross-cutting system challenges,
including scalability, cybersecurity, and privacy, and synthesize recent
model families across five functional research areas: (1) MAS for Complex
Problem Solving and Planning; (2) Embodied MAS for Physical
Environments; (3) MAS for Emergent Communication and Decentralized
Coordination; (4) MAS for Human-AI Teaming and Social Intelligence;
(5) Toward Generalist and Multi-tasking MAS. This work aims to
consolidate the fragmented literature, highlight common challenges, and
outline future research opportunities for developing scalable, general, and
interoperable MAS systems.