A Survey on Context-Aware Multi-Agent Systems: Techniques, Challenges and Future Directions

2024-02-03

arXiv.org

AI Summary

Robots exhibit insufficient autonomous decision-making capabilities in complex, dynamic environments—particularly regarding uncertainty modeling and cross-domain coordination. Method: This paper proposes a Context-Aware Multi-Agent System (CAMAS) framework. It introduces, for the first time, a cross-domain taxonomy and integration paradigm that explicitly aligns key attributes of context awareness and multi-agent systems. The framework integrates dynamic context modeling, distributed reasoning, lightweight context-aware learning, and large language model (LLM)-enhanced semantic understanding, while embedding domain-specific knowledge to improve generalizability and interpretability. Contributions: (1) A generic context-processing pipeline validated across six application domains—including autonomous driving and disaster response; (2) A systematic identification of core challenges: real-time responsiveness, heterogeneous integration, and trustworthy collaboration; and (3) A forward-looking discussion on scalable architectures, adaptive decision-making, and human–robot co-intelligence as critical future research directions.

Technology Category

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

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Context-aware Robotics
• Multi-agent Systems
• Autonomous Problem Solving

Innovation

• context-aware robots
• multi-agent systems
• state-of-the-art overview