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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.11122 |
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| _version_ | 1866913692418834432 |
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| author | Li, Zongyuan Lu, Chang Xu, Xiaojie Qi, Runnan Ni, Yanan Jiang, Lumin Liu, Xiangbei Zhang, Xuebo Fang, Yongchun Huang, Kuihua Guo, Xian |
| author_facet | Li, Zongyuan Lu, Chang Xu, Xiaojie Qi, Runnan Ni, Yanan Jiang, Lumin Liu, Xiangbei Zhang, Xuebo Fang, Yongchun Huang, Kuihua Guo, Xian |
| contents | Since the emergence of the Large Language Model (LLM), LLM has been widely used in fields such as writing, translating, and searching. However, there is still great potential for LLM-based methods in handling complex tasks such as decision-making in the StarCraft II environment. To address problems such as lack of relevant knowledge and poor control over subtasks of varying importance, we propose a Hierarchical Expert Prompt (HEP) for LLM. Our method improves the understanding of game situations through expert-level tactical knowledge, improving the processing quality of tasks of varying importance through a hierarchical framework. Our approach defeated the highest level (Elite) standard built-in agent in TextStarCraft II for the first time and consistently outperformed the baseline method in other difficulties. Our experiments suggest that the proposed method is a practical solution for tackling complex decision-making challenges. The replay video can be viewed on https://www.bilibili.com/video/BV1uz42187EF and https://youtu.be/dO3PshWLV5M, and our codes have been open-sourced on https://github.com/luchang1113/HEP-LLM-play-StarCraftII. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11122 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical Expert Prompt for Large-Language-Model: An Approach Defeat Elite AI in TextStarCraft II for the First Time Li, Zongyuan Lu, Chang Xu, Xiaojie Qi, Runnan Ni, Yanan Jiang, Lumin Liu, Xiangbei Zhang, Xuebo Fang, Yongchun Huang, Kuihua Guo, Xian Artificial Intelligence Since the emergence of the Large Language Model (LLM), LLM has been widely used in fields such as writing, translating, and searching. However, there is still great potential for LLM-based methods in handling complex tasks such as decision-making in the StarCraft II environment. To address problems such as lack of relevant knowledge and poor control over subtasks of varying importance, we propose a Hierarchical Expert Prompt (HEP) for LLM. Our method improves the understanding of game situations through expert-level tactical knowledge, improving the processing quality of tasks of varying importance through a hierarchical framework. Our approach defeated the highest level (Elite) standard built-in agent in TextStarCraft II for the first time and consistently outperformed the baseline method in other difficulties. Our experiments suggest that the proposed method is a practical solution for tackling complex decision-making challenges. The replay video can be viewed on https://www.bilibili.com/video/BV1uz42187EF and https://youtu.be/dO3PshWLV5M, and our codes have been open-sourced on https://github.com/luchang1113/HEP-LLM-play-StarCraftII. |
| title | Hierarchical Expert Prompt for Large-Language-Model: An Approach Defeat Elite AI in TextStarCraft II for the First Time |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2502.11122 |