LLM-mediated Dynamic Plan Generation with a Multi-Agent Approach
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912305711677440 |
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| author | Abe, Reo Ito, Akifumi Takayasu, Kanata Kurihara, Satoshi |
| author_facet | Abe, Reo Ito, Akifumi Takayasu, Kanata Kurihara, Satoshi |
| contents | Planning methods with high adaptability to dynamic environments are crucial for the development of autonomous and versatile robots. We propose a method for leveraging a large language model (GPT-4o) to automatically generate networks capable of adapting to dynamic environments. The proposed method collects environmental "status," representing conditions and goals, and uses them to generate agents. These agents are interconnected on the basis of specific conditions, resulting in networks that combine flexibility and generality. We conducted evaluation experiments to compare the networks automatically generated with the proposed method with manually constructed ones, confirming the comprehensiveness of the proposed method's networks and their higher generality. This research marks a significant advancement toward the development of versatile planning methods applicable to robotics, autonomous vehicles, smart systems, and other complex environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01637 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM-mediated Dynamic Plan Generation with a Multi-Agent Approach Abe, Reo Ito, Akifumi Takayasu, Kanata Kurihara, Satoshi Artificial Intelligence Robotics Planning methods with high adaptability to dynamic environments are crucial for the development of autonomous and versatile robots. We propose a method for leveraging a large language model (GPT-4o) to automatically generate networks capable of adapting to dynamic environments. The proposed method collects environmental "status," representing conditions and goals, and uses them to generate agents. These agents are interconnected on the basis of specific conditions, resulting in networks that combine flexibility and generality. We conducted evaluation experiments to compare the networks automatically generated with the proposed method with manually constructed ones, confirming the comprehensiveness of the proposed method's networks and their higher generality. This research marks a significant advancement toward the development of versatile planning methods applicable to robotics, autonomous vehicles, smart systems, and other complex environments. |
| title | LLM-mediated Dynamic Plan Generation with a Multi-Agent Approach |
| topic | Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2504.01637 |