Summary of Agent-oriented Planning in Multi-agent Systems, by Ao Li et al.
Agent-Oriented Planning in Multi-Agent Systems
by Ao Li, Yuexiang Xie, Songze Li, Fugee Tsung, Bolin Ding, Yaliang Li
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); 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 This paper presents a novel framework for multi-agent systems, called AOP (Agent-Oriented Planning), which leverages LLM-empowered agents and decomposes user queries into sub-tasks that can be allocated to suitable agents. The framework is designed around three critical principles: solvability, completeness, and non-redundancy, ensuring effective task resolution and satisfactory responses. AOP incorporates a fast task decomposition and allocation process, an efficient evaluation via a reward model, and a feedback loop for iterative improvement. Experimental results demonstrate the superiority of AOP in solving real-world problems compared to single-agent systems and existing planning strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can work together to solve big problems. It’s like having a team of experts working together to answer your questions. The main idea is to break down hard problems into smaller, easier tasks that different “agents” can handle. The agents use special tools and expertise to figure out the answers. The researchers came up with three important rules to make sure this works well: making sure each task can be solved, making sure all the necessary tasks are covered, and not repeating work. They also created a new way of planning called AOP that uses these rules and makes adjustments as needed. This helps the agents give better answers and solve problems more efficiently. |
