How can shared action suggestions improve multi-agent planning efficiency?
Efficient Multiagent Planning via Shared Action Suggestions
This paper proposes a novel approach to multi-agent planning under uncertainty where agents communicate suggested actions instead of sharing all observations and beliefs, reducing complexity while maintaining performance comparable to centralized planning. The algorithm estimates joint beliefs by pruning infeasible beliefs based on received action suggestions, mirroring how humans often collaborate.
This approach is relevant to LLM-based multi-agent systems as it provides a more efficient communication mechanism compared to full belief sharing. By inferring beliefs from suggested actions, similar to interpreting natural language instructions, it offers a path toward scalable multi-agent coordination, especially when using pre-trained, computationally expensive LLMs where frequent communication or extensive prompt engineering is undesirable. The method's alignment with human communication patterns also suggests its potential for human-agent teaming scenarios involving LLMs.