Agent-Oriented Planning in Multi-Agent Systems
- URL: http://arxiv.org/abs/2410.02189v1
- Date: Thu, 3 Oct 2024 04:07:51 GMT
- Title: Agent-Oriented Planning in Multi-Agent Systems
- Authors: Ao Li, Yuexiang Xie, Songze Li, Fugee Tsung, Bolin Ding, Yaliang Li,
- Abstract summary: We propose a novel framework for agent-oriented planning in multi-agent systems, leveraging a fast task decomposition and allocation process.
We integrate a feedback loop into the proposed framework to further enhance the effectiveness and robustness of such a problem-solving process.
- Score: 54.429028104022066
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Through the collaboration of multiple agents possessing diverse expertise and tools, multi-agent systems achieve impressive progress in solving real-world problems. Given the user queries, the meta-agents, serving as the brain within these systems, are required to decompose the queries into multiple sub-tasks that can be allocated to suitable agents capable of solving them, so-called agent-oriented planning. In this study, we identify three critical design principles of agent-oriented planning, including solvability, completeness, and non-redundancy, to ensure that each sub-task is effectively resolved, leading to satisfactory responses to the original queries. These principles further inspire us to propose a novel framework for agent-oriented planning in multi-agent systems, leveraging a fast task decomposition and allocation process followed by an effective and efficient evaluation via a reward model. During the planning process, the meta-agent is also responsible for evaluating the performance of the expert agents, making timely adjustments to the sub-tasks and scheduling as necessary. Besides, we integrate a feedback loop into the proposed framework to further enhance the effectiveness and robustness of such a problem-solving process. Extensive experiments demonstrate the advancement of the proposed framework in solving real-world problems compared to both single-agent systems and existing planning strategies for multi-agent systems.
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