How can LLMs improve multi-agent decision-making?
A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives
This paper surveys the field of multi-agent cooperative decision-making, exploring different approaches and their applications. It focuses on how multiple AI agents can work together to achieve a common goal.
Key points for LLM-based multi-agent systems: LLMs enhance communication and collaboration between agents; hierarchical agent organization (global planners and local executors) improves task management; new platforms and environments like TDW-MAT, C-WAH, Cuisineworld, and AgentScope enable testing and development; LLMs face challenges in multi-agent settings regarding multi-modal integration, hallucination, collective intelligence acquisition, scalability, evaluation, and security/privacy. LLMs play different roles in the LLM-enhanced MARL framework, such as information processor, reward designer, decision-maker, and generator. They enable better multi-modal information processing, multi-task learning, and long-term planning. Integrating LLMs and MARL expands applications in areas like autonomous driving and collaborative robots. Future research should address ethical considerations like bias and security.