Real-time automata (RTAs) are a subclass of timed automata with only one clock which resets at each transition. In this paper, we present an active learning algorithm for deterministic real-time automata (DRTAs) in both continuous-time semantics and discrete-time semantics. For a target language recognized by a DRTA \(\mathcal{A}\), we convert the problem of learning DRTA \(\mathcal{A}\) to the problem of learning a canonical real-time automaton \(\mathbb{A}\) with the same recognized language, i.e., \(\mathcal{L}(\mathbb{A})=\mathcal{L}(\mathcal{A})\). The algorithm is inspired by existing learning algorithms for symbolic automata.

实时自动机 (RTA) 是定时自动机的一个子类，只有一个时钟在每次转换时重置。在本文中，我们提出了一种用于连续时间语义和离散时间语义的确定性实时自动机（DRTA）的主动学习算法。对于 DRTA \(\mathcal{A}\)识别的目标语言，我们将学习 DRTA \(\mathcal{A}\)的问题转换为学习规范的实时自动机\(\mathbb{ A}\)使用相同的识别语言，即\(\mathcal{L}(\mathbb{A})=\mathcal{L}(\mathcal{A})\)。该算法的灵感来自现有的符号自动机学习算法。