LLM-SAP: Large Language Models Situational Awareness Based Planning
It addresses the need for more reliable and safe AI systems in unpredictable real-world applications, though it appears incremental by combining existing methods.
This study tackled the problem of enhancing AI decision-making in dynamic environments by integrating large language models with situational awareness-based planning, resulting in significant improvements in providing safe actions during hazard interactions.
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.