Summary of Towards Collaborative Intelligence: Propagating Intentions and Reasoning For Multi-agent Coordination with Large Language Models, by Xihe Qiu et al.
Towards Collaborative Intelligence: Propagating Intentions and Reasoning for Multi-Agent Coordination with Large Language Models
by Xihe Qiu, Haoyu Wang, Xiaoyu Tan, Chao Qu, Yujie Xiong, Yuan Cheng, Yinghui Xu, Wei Chu, Yuan Qi
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The presented framework enables large language models (LLMs) to be trained as collaborative agents for multi-agent reinforcement learning (MARL). The framework addresses current agent frameworks’ limitations by providing robust inter-module communication, allowing agents to infer coordination tasks and enable coordinated behaviors. The architecture consists of planning, grounding, and execution modules, with the grounding module dynamically adapting comprehension strategies based on emerging coordination patterns. Feedback from execution agents influences the planning module, enabling dynamic re-planning of sub-tasks. Results in collaborative environment simulation demonstrate intention propagation reduces miscoordination errors by aligning sub-task dependencies between agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A group of artificial intelligence agents work together to achieve a common goal. They share information about what they want to do and what’s important for them to accomplish this goal. This helps the agents stay coordinated and avoid mistakes. The system has three parts: planning, understanding, and taking action. As the agents take actions, they learn from each other and adjust their plans accordingly. This allows them to work together more effectively and achieve better results. |
Keywords
» Artificial intelligence » Grounding » Reinforcement learning
