The problem considered in this paper is the online diagnosis of Automated
Production Systems with sensors and actuators delivering discrete binary
signals that can be modeled as Discrete Event Systems. Even though there are
numerous diagnosis methods, none of them can meet all the criteria of
implementing an efficient diagnosis system (such as an intelligent solution, an
average effort, a reasonable cost, an online diagnosis, fewer false alarms,
etc.). In addition, these techniques require either a correct, robust, and
representative model of the system or relevant data or experts' knowledge that
require continuous updates. In this paper, we propose a Machine Learning-based
approach of a diagnostic system. It is considered as a multi-class classifier
that predicts the plant state: normal or faulty and what fault that has arisen
in the case of failing behavior.

本文提出了一种基于机器学习的在线诊断系统，用于预测自动生产系统的状态和故障类型，并可将其分类为多类别。

机器学习方法用于离散事件系统的在线故障诊断

Machine learning-based approach for online fault Diagnosis of Discrete Event System

Directed Controller Synthesis technique finds solutions for the non-blocking
property in discrete event systems by exploring a reduced portion of the
exponentially big state space, using best-first search. Aiming to minimize the
explored states, it is currently guided by a domain-independent handcrafted
heuristic, with which it reaches state-of-the-art performance. In this work, we
propose a new method for obtaining heuristics based on Reinforcement Learning.
The synthesis algorithm is framed as an RL task with an unbounded action space
and a modified version of DQN is used. With a simple and general set of
features, we show that it is possible to learn heuristics on small versions of
a problem in a way that generalizes to the larger instances. Our agents learn
from scratch and outperform the existing heuristic overall, in instances unseen
during training.

本研究提出使用强化学习基于状态特征学习启发式启发式方法且优化了 Deep Q-Network，以用于解决离散事件系统中的无阻塞属性，实现在小规模问题上的学习推广到更大规模问题实例，结果表明其效果更好。

强化学习在定向控制器合成中的扩展应用

Scaling Directed Controller Synthesis via Reinforcement Learning

Discrete event systems (DES) have been established and deeply developed in
the framework of probabilistic and fuzzy computing models due to the necessity
of practical applications in fuzzy and probabilistic systems. With the
development of quantum computing and quantum control, a natural problem is to
simulate DES by means of quantum computing models and to establish {\it quantum
DES} (QDES). The motivation is twofold: on the one hand, QDES have potential
applications when DES are simulated and processed by quantum computers, where
quantum systems are employed to simulate the evolution of states driven by
discrete events, and on the other hand, QDES may have essential advantages over
DES concerning state complexity for imitating some practical problems. The goal
of this paper is to establish a basic framework of QDES by using {\it quantum
finite automata} (QFA) as the modelling formalisms, and the supervisory control
theorems of QDES are established and proved. Then we present a polynomial-time
algorithm to decide whether or not the controllability condition holds. In
particular, we construct a number of new examples of QFA to illustrate the
supervisory control of QDES and to verify the essential advantages of QDES over
DES in state complexity.

本文旨在利用量子有限自动机 (QFA) 作为建模形式建立量子离散事件系统 (QDES) 的基本框架，并对 QDES 的监督控制定理进行建立和证明。同时，我们介绍了一种用于判断可控性条件是否成立的多项式时间算法。特别地，我们构造了许多新的 QFA 示例，以说明 QDES 在状态复杂性方面的优势。

量子离散事件系统的监督控制

Supervisory Control of Quantum Discrete Event Systems

In order to cope with situations in which a plant's dynamics are not
precisely known, we consider the problem of supervisory control for a class of
discrete event systems modelled by fuzzy automata. The behavior of such
discrete event systems is described by fuzzy languages; the supervisors are
event feedback and can disable only controllable events with any degree. The
concept of discrete event system controllability is thus extended by
incorporating fuzziness. In this new sense, we present a necessary and
sufficient condition for a fuzzy language to be controllable. We also study the
supremal controllable fuzzy sublanguage and the infimal controllable fuzzy
superlanguage when a given pre-specified desired fuzzy language is
uncontrollable. Our framework generalizes that of Ramadge-Wonham and reduces to
Ramadge-Wonham framework when membership grades in all fuzzy languages must be
either 0 or 1. The theoretical development is accompanied by illustrative
numerical examples.

针对不确定植物动态的情况，研究基于模糊自动机模型的离散事件系统的监督控制问题，并将模糊性纳入到离散事件系统可控性的概念中，给出了一个模糊语言可控的充分必要条件，研究了当指定模糊语言不可控时的最大可控模糊子语言和最小可控模糊超语言。