We present the first algorithm for query learning of a general class of Mealy
machines with timers (MMTs) in a black-box context. Our algorithm is an
extension of the L# algorithm of Vaandrager et al. to a timed setting. Like the
algorithm for learning timed automata proposed by Waga, our algorithm is
inspired by ideas of Maler & Pnueli. Based on the elementary languages of, both
Waga's and our algorithm use symbolic queries, which are then implemented using
finitely many concrete queries. However, whereas Waga needs exponentially many
concrete queries to implement a single symbolic query, we only need a
polynomial number. This is because in order to learn a timed automaton, a
learner needs to determine the exact guard and reset for each transition (out
of exponentially many possibilities), whereas for learning an MMT a learner
only needs to figure out which of the preceding transitions caused a timeout.
As shown in our previous work, this can be done efficiently for a subclass of
MMTs that are race-avoiding: if a timeout is caused by a preceding input then a
slight change in the timing of this input will induce a corresponding change in
the timing of the timeout ("wiggling"). Experiments with a prototype
implementation, written in Rust, show that our algorithm is able to efficiently
learn realistic benchmarks.

我们提出了第一个用于黑盒上下文中的具有计时器的 Mealy 机组的查询学习算法。该算法扩展了 Vaandrager 等人的 L＃算法，以应用于定时设置。通过使用符号查询实现的我们的算法仅需要多项式数量的具体查询，相较于 Waga 的算法的指数数量的具体查询，该算法能有效学习实际基准测试组。

具有计时器的 Mealy 机器的主动学习

Active Learning of Mealy Machines with Timers

Model-Based Anomaly Detection has been a successful approach to identify
deviations from the expected behavior of Cyber-Physical Production Systems.
Since manual creation of these models is a time-consuming process, it is
advantageous to learn them from data and represent them in a generic formalism
like timed automata. However, these models - and by extension, the detected
anomalies - can be challenging to interpret due to a lack of additional
information about the system. This paper aims to improve model-based anomaly
detection in CPPS by combining the learned timed automaton with a formal
knowledge graph about the system. Both the model and the detected anomalies are
described in the knowledge graph in order to allow operators an easier
interpretation of the model and the detected anomalies. The authors
additionally propose an ontology of the necessary concepts. The approach was
validated on a five-tank mixing CPPS and was able to formally define both
automata model as well as timing anomalies in automata execution.

基于模型的异常检测通过将学习到的时序自动机与系统相关的形式知识图谱相结合，提供了对模型和检测到的异常的更简单的解释和理解，并提出了必要概念的本体论，该方法在一个五罐混合 CPPS 上进行验证，并能正式定义自动机模型和自动机执行的时间异常。

在知识图谱中表示定时自动机和物理系统的时间异常

Representing Timed Automata and Timing Anomalies of Cyber-Physical  Production Systems in Knowledge Graphs

Recurrent Neural Networks (RNNs) have shown great success in modeling
time-dependent patterns, but there is limited research on their learned
representations of latent temporal features and the emergence of these
representations during training. To address this gap, we use timed automata
(TA) to introduce a family of supervised learning tasks modeling behavior
dependent on hidden temporal variables whose complexity is directly
controllable. Building upon past studies from the perspective of dynamical
systems, we train RNNs to emulate temporal flipflops, a new collection of TA
that emphasizes the need for time-awareness over long-term memory. We find that
these RNNs learn in phases: they quickly perfect any time-independent behavior,
but they initially struggle to discover the hidden time-dependent features. In
the case of periodic "time-of-day" aware automata, we show that the RNNs learn
to switch between periodic orbits that encode time modulo the period of the
transition rules. We subsequently apply fixed point stability analysis to
monitor changes in the RNN dynamics during training, and we observe that the
learning phases are separated by a bifurcation from which the periodic behavior
emerges. In this way, we demonstrate how dynamical systems theory can provide
insights into not only the learned representations of these models, but also
the dynamics of the learning process itself. We argue that this style of
analysis may provide insights into the training pathologies of recurrent
architectures in contexts outside of time-awareness.

本文利用定时自动机来介绍一系列监督学习任务，建立在隐藏时间变量和复杂性直接可控的行为模型基础上，通过研究倒置的时间特征，证明基于动态系统理论的工具可以不仅提供模型学习方案的见解而且能够证明训练过程的动力学。