PIANIST: Learning Partially Observable World Models with LLMs for Multi-Agent Decision Making
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929603776348160 |
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| author | Light, Jonathan Xing, Sixue Liu, Yuanzhe Chen, Weiqin Cai, Min Chen, Xiusi Wang, Guanzhi Cheng, Wei Yue, Yisong Hu, Ziniu |
| author_facet | Light, Jonathan Xing, Sixue Liu, Yuanzhe Chen, Weiqin Cai, Min Chen, Xiusi Wang, Guanzhi Cheng, Wei Yue, Yisong Hu, Ziniu |
| contents | Effective extraction of the world knowledge in LLMs for complex decision-making tasks remains a challenge. We propose a framework PIANIST for decomposing the world model into seven intuitive components conducive to zero-shot LLM generation. Given only the natural language description of the game and how input observations are formatted, our method can generate a working world model for fast and efficient MCTS simulation. We show that our method works well on two different games that challenge the planning and decision making skills of the agent for both language and non-language based action taking, without any training on domain-specific training data or explicitly defined world model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_15998 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PIANIST: Learning Partially Observable World Models with LLMs for Multi-Agent Decision Making Light, Jonathan Xing, Sixue Liu, Yuanzhe Chen, Weiqin Cai, Min Chen, Xiusi Wang, Guanzhi Cheng, Wei Yue, Yisong Hu, Ziniu Artificial Intelligence Machine Learning Multiagent Systems Effective extraction of the world knowledge in LLMs for complex decision-making tasks remains a challenge. We propose a framework PIANIST for decomposing the world model into seven intuitive components conducive to zero-shot LLM generation. Given only the natural language description of the game and how input observations are formatted, our method can generate a working world model for fast and efficient MCTS simulation. We show that our method works well on two different games that challenge the planning and decision making skills of the agent for both language and non-language based action taking, without any training on domain-specific training data or explicitly defined world model. |
| title | PIANIST: Learning Partially Observable World Models with LLMs for Multi-Agent Decision Making |
| topic | Artificial Intelligence Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2411.15998 |