Advanced Air Mobility (AAM) is a growing field that demands accurate and trustworthy models of legal concepts and restrictions for navigating Unmanned Aircraft Systems (UAS). In addition, any implementation of AAM needs to face the challenges posed by inherently dynamic and uncertain human-inhabited spaces robustly. Nevertheless, the employment of UAS beyond visual line of sight (BVLOS) is an endearing task that promises to significantly enhance today's logistics and emergency response capabilities. Hence, we propose Probabilistic Mission Design (ProMis), a novel neuro-symbolic approach to navigating UAS within legal frameworks. ProMis is an interpretable and adaptable system architecture that links uncertain geospatial data and noisy perception with declarative, Hybrid Probabilistic Logic Programs (HPLP) to reason over the agent's state space and its legality. To inform planning with legal restrictions and uncertainty in mind, ProMis yields Probabilistic Mission Landscapes (PML). These scalar fields quantify the belief that the HPLP is satisfied across the agent's state space. Extending prior work on ProMis' reasoning capabilities and computational characteristics, we show its integration with potent machine learning models such as Large Language Models (LLM) and Transformer-based vision models. Hence, our experiments underpin the application of ProMis with multi-modal input data and how our method applies to many AAM scenarios.

翻译：先进空中交通（AAM）是一个不断发展的领域，它要求对法律概念和限制条件进行精确且可信的建模，以引导无人机系统（UAS）的航行。此外，任何AAM的实施都需要稳健地应对人类居住空间固有的动态性和不确定性所带来的挑战。尽管如此，超视距（BVLOS）无人机系统的应用是一项极具吸引力的任务，有望显著提升当今的物流和应急响应能力。因此，我们提出了概率任务设计（ProMis），一种在法律法规框架内导航UAS的新型神经符号方法。ProMis是一个可解释且适应性强的系统架构，它将不确定的地理空间数据和带有噪声的感知，与声明式的混合概率逻辑程序（HPLP）相连接，以对智能体的状态空间及其合法性进行推理。为了在规划时考虑法律限制和不确定性，ProMis生成概率任务态势图（PML）。这些标量场量化了HPLP在智能体整个状态空间内得到满足的置信度。通过扩展先前关于ProMis推理能力和计算特性的工作，我们展示了其与大型语言模型（LLM）和基于Transformer的视觉模型等强大机器学习模型的集成。因此，我们的实验验证了ProMis在多模态输入数据下的应用，以及我们的方法如何适用于多种AAM场景。