Active automata learning in the framework of Angluin's $L^*$ algorithm has been applied to learning many kinds of automata models. In applications to timed models such as timed automata, the main challenge is to determine guards on the clock value in transitions as well as which transitions reset the clock. In this paper, we introduce a new algorithm for active learning of deterministic one-clock timed automata and timed Mealy machines. The algorithm uses observation tables that do not commit to specific choices of reset, but instead rely on constraint solving to determine reset choices that satisfy readiness conditions. We evaluate our algorithm on randomly-generated examples as well as practical case studies, showing that it is applicable to larger models, and competitive with existing work for learning other forms of timed models.
翻译:在安格鲁因的 $L $ $$ 的算法框架内, 主动自动磁体学习已被应用于学习多种自动磁体模型。 在时间化自动磁体等定时模型的应用中, 主要的挑战是如何确定过渡中时钟值的卫士以及转换时钟。 在本文中, 我们引入了一种新的算法, 用于积极学习确定性1小时计时自动磁体和定时米利机器。 算法使用的观察表不承诺选择重置的具体选择, 而是依赖约束性解决方案来确定符合准备状态条件的选择重置。 我们评估了随机生成的示例的算法以及实际案例研究, 表明它适用于更大的模型, 并且与现有的学习其他定时模型的工作相比具有竞争力 。