A Survey on Context-Aware Multi-Agent Systems: Techniques, Challenges
  and Future Directions
Hung Du Srikanth Thudumu Rajesh Vasa Kon Mouzakis
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.