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.

翻译：基础模型（包括大语言模型）正日益被用于人机协同信息物理系统，这是因为基于基础模型的人工智能代理能够同时与物理环境和人类用户进行交互。然而，人类用户和人工智能代理的不可预测行为，加之动态变化的物理环境，导致了不可控的不确定性。为应对这一紧迫挑战，实现基于Agentic人工智能的人机协同信息物理系统，我们提出了一种基于反应式计算模型的方法，并通过开源框架Lingua Franca加以实现。我们还以智能驾驶教练作为人机协同信息物理系统的具体应用场景进行了案例研究。通过对基于Lingua Franca的Agentic人机协同信息物理系统进行评估，我们识别出在此类系统中重建确定性所面临的实际挑战，并提出了解决路径。