Summary of Capo: Cooperative Plan Optimization For Efficient Embodied Multi-agent Cooperation, by Jie Liu et al.
CaPo: Cooperative Plan Optimization for Efficient Embodied Multi-Agent Cooperation
by Jie Liu, Pan Zhou, Yingjun Du, Ah-Hwee Tan, Cees G.M. Snoek, Jan-Jakob Sonke, Efstratios Gavves
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Multiagent Systems (cs.MA)
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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 proposed Cooperative Plan Optimization (CaPo) framework enhances the cooperation efficiency of large language model (LLM)-based embodied agents in complex tasks like search-and-rescue missions. By combining meta-plan generation and progress-adaptive execution, CaPo improves upon previous methods that often execute actions extemporaneously and incoherently. The two-phase approach involves analyzing the task, discussing, and cooperatively creating a long-term strategic plan, followed by adapting and executing the plan based on agents’ progress. Experimental results on the ThreeDworld Multi-Agent Transport and Communicative Watch-And-Help tasks demonstrate CaPo’s effectiveness in achieving higher task completion rates and efficiency compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cooperative Plan Optimization (CaPo) is a new way for machines to work together better. Right now, computers that can learn like humans (called large language models or LLMs) don’t do a great job of working together when they need to achieve something. They just do things without planning ahead and often make mistakes. CaPo helps these machines plan and work together more effectively by having them discuss and agree on a shared goal, then adjust their actions based on how well they’re doing. This makes it easier for the machines to complete tasks like finding people in a disaster zone. |
Keywords
» Artificial intelligence » Large language model » Optimization
