基于概率的方法用于神经符号无人机系统任务设计
Probabilistic Mission Design for Neuro-Symbolic Unmanned Aircraft Systems
AI总结：
本文提出概率任务设计（ProMis）方法，结合神经符号技术，用于在法律框架内导航无人驾驶航空器，通过混合概率逻辑程序和机器学习模型提升任务规划的可靠性和适应性。
摘要：
先进空中交通（AAM）是一个快速发展的领域，要求准确且可信地建模法律概念和限制，以导航无人驾驶航空器（UAS）。此外，任何AAM的实施都需要在本质上动态且不确定的人类居住空间中稳健地应对挑战。然而，无人驾驶航空器在视距外（BVLOS）的使用是一项引人注目的任务，有望显著增强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），一种新颖的神经符号方法，用于在法律框架内导航UAS。ProMis是一种可解释且可适应的系统架构，它将不确定的地理空间数据和嘈杂的感知与声明性、混合概率逻辑程序（HPLP）联系起来，以推断代理的状态空间及其合法性。为了在考虑法律限制和不确定性的前提下进行规划，ProMis生成概率任务景观（PML）。这些标量场量化了HPLP在代理状态空间中被满足的信念。在扩展ProMis推理能力和计算特性的同时，我们展示了其与强大机器学习模型如大型语言模型（LLM）和基于Transformer的视觉模型的结合。因此，我们的实验支持了ProMis在多模态输入数据中的应用，以及我们的方法在许多AAM场景中的应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
