Unmanned aircraft systems (UAS) collaborate with humans to operate in diverse, safety-critical applications. However, assurance technologies need to be integrated into the design process in order to guarantee safe behavior, thereby enabling UAS operations in the National Airspace System (NAS). In this paper, formal methods are integrated with learning-enabled systems representations. The generation and representation of knowledge are captured via monadic second-order logic rules in the cognitive architecture Soar. These rules are translated into timed automata, and a proof of correctness for the translation is provided so that safety and liveness properties can be checked in the formal verification environment Uppaal. This approach is agnostic to the learning mechanism used to generate the learned rules (e.g., chunking, etc.). An example of a fault-tolerant, learning-enabled UAS deciding which of four contingency procedures to execute under a lost link scenario while overflying an urban area is used to illustrate the approach.

中文翻译：

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确保智能系统：无人机应急管理


无人机系统 (UAS) 与人类协作，在各种安全关键型应用中运行。然而，保障技术需要集成到设计过程中，以保证安全行为，从而使无人机能够在国家空域系统（NAS）中运行。在本文中，形式化方法与支持学习的系统表示相集成。知识的生成和表示是通过认知架构 Soar 中的一元二阶逻辑规则捕获的。这些规则被翻译成定时自动机，并提供翻译正确性证明，以便可以在形式验证环境 Uppaal 中检查安全性和活性属性。这种方法与用于生成学习规则（例如，分块等）的学习机制无关。一个容错、具有学习功能的无人机系统在飞越市区时决定在丢失链路情况下执行四种应急程序中的哪一种来说明该方法。