Research interest in autonomous agents is on the rise as an emerging topic. The notable achievements of Large Language Models (LLMs) have demonstrated the considerable potential to attain human-like intelligence in autonomous agents. However, the challenge lies in enabling these agents to learn, reason, and navigate uncertainties in dynamic environments. Context awareness emerges as a pivotal element in fortifying multi-agent systems when dealing with dynamic situations. Despite existing research focusing on both context-aware systems and multi-agent systems, there is a lack of comprehensive surveys outlining techniques for integrating context-aware systems with multi-agent systems. To address this gap, this survey provides a comprehensive overview of state-of-the-art context-aware multi-agent systems. First, we outline the properties of both context-aware systems and multi-agent systems that facilitate integration between these systems. Subsequently, we propose a general process for context-aware systems, with each phase of the process encompassing diverse approaches drawn from various application domains such as collision avoidance in autonomous driving, disaster relief management, utility management, supply chain management, human-AI interaction, and others. Finally, we discuss the existing challenges of context-aware multi-agent systems and provide future research directions in this field.

翻译：自主智能体的研究兴趣正作为一个新兴主题日益增长。大语言模型（LLMs）的显著成就已展现出自主智能体实现类人智能的巨大潜力。然而，挑战在于如何使这些智能体在动态环境中学习、推理并应对不确定性。上下文感知在处理动态场景时成为增强多智能体系统的关键要素。尽管现有研究聚焦于上下文感知系统与多智能体系统，但仍缺乏系统阐述上下文感知系统与多智能体系统集成技术的全面综述。为填补这一空白，本综述对最先进的上下文感知多智能体系统进行了全面概述。首先，我们梳理了促进上下文感知系统与多智能体系统集成的系统特性。其次，提出了上下文感知系统的通用流程，其中每个阶段均涵盖来自自动驾驶碰撞规避、灾害救援管理、公用事业管理、供应链管理、人机交互等不同应用领域的多样化方法。最后，我们探讨了上下文感知多智能体系统当前面临的挑战，并指出了该领域的未来研究方向。