Discrete-event systems usually consist of discrete states and transitions between them caused by spontaneous occurrences of labelled (aka partially-observed) events. Due to the partially-observed feature, fundamental properties therein could be classified into two categories: state/event-inference-based properties (e.g., strong detectability, diagnosability, and predictability) and state-concealment-based properties (e.g., opacity). Intuitively, the former category describes whether one can use observed output sequences to infer the current and subsequent states, past occurrences of faulty events, or future certain occurrences of faulty events; while the latter describes whether one cannot use observed output sequences to infer whether some secret states have been visited (that is, whether the DES can conceal the status that its secret states have been visited). Over the past two decades these properties were studied separately using different methods. In this review article, for labeled finite-state automata, a unified concurrent-composition method is shown to verify all above inference-based properties and concealment-based properties, resulting in a unified mathematical framework for the two categories of properties. In addition, compared with the previous methods in the literature, the concurrent-composition method does not depend on assumptions and is more efficient.

翻译：由于部分观测到的特征,其中的基本属性可分为两类:基于状态/事件的属性(例如,强可探测性、可诊断性和可预测性)和基于状态的封闭性属性(例如,不透明性)。在审查文章中,对于标定的有限状态自动自动数据,采用统一的并列法方法可以核实所有以上基于推断的属性和隐藏性能,而在比较前两种性质时,采用的统一的数学假设并不取决于先前两种方法。