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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2312.16127 |
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| _version_ | 1866929387655397376 |
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| author | Wang, Liman Zhong, Hanyang |
| author_facet | Wang, Liman Zhong, Hanyang |
| contents | This study explores integrating large language models (LLMs) with situational awareness-based planning (SAP) to enhance the decision-making capabilities of AI agents in dynamic and uncertain environments. We employ a multi-agent reasoning framework to develop a methodology that anticipates and actively mitigates potential risks through iterative feedback and evaluation processes. Our approach diverges from traditional automata theory by incorporating the complexity of human-centric interactions into the planning process, thereby expanding the planning scope of LLMs beyond structured and predictable scenarios. The results demonstrate significant improvements in the model's ability to provide comparative safe actions within hazard interactions, offering a perspective on proactive and reactive planning strategies. This research highlights the potential of LLMs to perform human-like action planning, thereby paving the way for more sophisticated, reliable, and safe AI systems in unpredictable real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_16127 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | LLM-SAP: Large Language Models Situational Awareness Based Planning Wang, Liman Zhong, Hanyang Artificial Intelligence This study explores integrating large language models (LLMs) with situational awareness-based planning (SAP) to enhance the decision-making capabilities of AI agents in dynamic and uncertain environments. We employ a multi-agent reasoning framework to develop a methodology that anticipates and actively mitigates potential risks through iterative feedback and evaluation processes. Our approach diverges from traditional automata theory by incorporating the complexity of human-centric interactions into the planning process, thereby expanding the planning scope of LLMs beyond structured and predictable scenarios. The results demonstrate significant improvements in the model's ability to provide comparative safe actions within hazard interactions, offering a perspective on proactive and reactive planning strategies. This research highlights the potential of LLMs to perform human-like action planning, thereby paving the way for more sophisticated, reliable, and safe AI systems in unpredictable real-world applications. |
| title | LLM-SAP: Large Language Models Situational Awareness Based Planning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2312.16127 |