Abstract
Exact inference in probabilistic First-Order Logic offers a promising yet computationally costly approach for regulating the behavior of autonomous agents in shared traffic spaces. While prior methods have combined logical and probabilistic data into decision-making frameworks, their application is often limited to pre-flight checks due to the complexity of reasoning across vast numbers of possible universes. In this work, we propose a reactive mission design framework that jointly considers uncertain environmental data and declarative, logical traffic regulations. By synthesizing Probabilistic Mission Design (ProMis) with reactive reasoning facilitated by Reactive Circuits (RC), we enable online, exact probabilistic inference over hybrid domains. Our approach leverages the Frequency of Change inherent in heterogeneous data streams to subdivide inference formulas into memoized, isolated tasks, ensuring that only the specific components affected by new sensor data are re-evaluated. In experiments involving both real-world vessel data and simulated drone traffic in dense urban scenarios, we demonstrate that our approach provides orders of magnitude in speedup over ProMis without reactive paradigms. This allows intelligent transportation systems, such as Unmanned Aircraft Systems (UAS), to actively assert safety and legal compliance during operations rather than relying solely on preparation procedures.
Abstract (translated)
在概率一阶逻辑中进行精确推理为调节共享交通空间中的自主代理行为提供了一种有前景但计算成本高昂的方法。尽管先前的方法将逻辑和概率数据结合到决策框架中，但由于跨大量可能世界的复杂推理问题，它们的应用往往仅限于飞行前检查。在此工作中，我们提出了一种反应式任务设计框架，该框架同时考虑不确定的环境数据和声明性的、基于逻辑的交通规则。通过将概率任务设计（ProMis）与由反应电路（RC）支持的反应性推理相结合，我们能够在混合领域中实现在线精确的概率推理。 我们的方法利用异构数据流中的变化频率，将推理公式分解为可以缓存并隔离的任务，从而确保仅重新评估受新传感器数据影响的具体部分。通过使用真实世界船只数据和密集城市环境中模拟无人机交通进行的实验，我们展示了与不采用反应式范式的ProMis相比，我们的方法在速度上提高了几个数量级。这使得智能运输系统（如无人驾驶飞机系统UAS）能够在操作期间主动确保安全性和合规性，而不仅仅是依赖于准备程序。 这段翻译保留了原文的主要内容和技术细节，并尽量使其通俗易懂。
URL
https://arxiv.org/abs/2603.03977
