用反应式计算框架让智能驾驶教练系统更稳定可靠
Agentic Driving Coach: Robustness and Determinism of Agentic AI-Powered Human-in-the-Loop Cyber-Physical Systems
- 基于反应式计算模型，通过开源Lingua Franca框架实现
- 实测发现该系统在动态环境中具备更强的确定性
- 适合研究人机协同智能系统的开发者和工程师
基础模型，包括大语言模型（LLMs），正越来越多地用于人机协同（HITL）网络物理系统（CPS），因为基于基础模型的智能体可能与物理环境和人类用户进行交互。然而，人类用户的不可预测行为、人工智能代理的不确定性以及不断变化的物理环境，导致了无法控制的非确定性。为应对这一迫切挑战，即实现基于智能体的AI驱动的人机协同网络物理系统，我们提出了一种基于反应式计算模型（MoC）的方法，并通过开源Lingua Franca（LF）框架实现。我们还以智能驾驶教练作为人机协同网络物理系统的具体应用案例进行了研究。通过对基于LF的智能体人机协同网络物理系统进行评估，我们识别出在重新引入确定性方面的实际挑战，并提出了相应的解决路径。
原文摘要 · Abstract (English)
Foundation models, including large language models (LLMs), are increasingly used for human-in-the-loop (HITL) cyber-physical systems (CPS) because foundation model-based AI agents can potentially interact with both the physical environments and human users. However, the unpredictable behavior of human users and AI agents, in addition to the dynamically changing physical environments, leads to uncontrollable nondeterminism. To address this urgent challenge of enabling agentic AI-powered HITL CPS, we propose a reactor-model-of-computation (MoC)-based approach, realized by the open-source Lingua Franca (LF) framework. We also carry out a concrete case study using the agentic driving coach as an application of HITL CPS. By evaluating the LF-based agentic HITL CPS, we identify practical challenges in reintroducing determinism into such agentic HITL CPS and present pathways to address them.
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