On Supervisor Synthesis via Active Automata Learning

Our society\u27s reliance on computer-controlled systems is rapidly growing. Such systems are found in various devices, ranging from simple light switches to safety-critical systems like autonomous vehicles. In the context of safety-critical systems, safety and correctness are of utmost importance. Faults and errors could have catastrophic consequences. Thus, there is a need for rigorous methodologies that help provide guarantees of safety and correctness. Supervisor synthesis, the concept of being able to mathematically synthesize a supervisor that ensures that the closed-loop system behaves in accordance with known requirements, can indeed help.This thesis introduces supervisor learning, an approach to help automate the learning of supervisors in the absence of plant models. Traditionally, supervisor synthesis makes use of plant models and specification models to obtain a supervisor. Industrial adoption of this method is limited due to, among other things, the difficulty in obtaining usable plant models. Manually creating these plant models is an error-prone and time-consuming process. Thus, supervisor learning intends to improve the industrial adoption of supervisory control by automating the process of generating supervisors in the absence of plant models.The idea here is to learn a supervisor for the system under learning (SUL) by active interaction and experimentation. To this end, we present two algorithms, SupL*, and MSL, that directly learn supervisors when provided with a simulator of the SUL and its corresponding specifications. SupL* is a language-based learner that learns one supervisor for the entire system. MSL, on the other hand, learns a modular supervisor, that is, several smaller supervisors, one for each specification. Additionally, a third algorithm, MPL, is introduced for learning a modular plant model.The approach is realized in the tool MIDES and has been used to learn supervisors in a virtual manufacturing setting for the Machine Buffer Machine example, as well as learning a model of the Lateral State Manager, a sub-component of a self-driving car. These case studies show the feasibility and applicability of the proposed approach, in addition to helping identify future directions for research

Modular Supervisory Synthesis for Unknown Plant Models Using Active Learning

This paper proposes an approach to synthesize a modular discrete-event supervisor to control a plant, the behavior model of which is unknown, so as to satisfy given specifications. To this end, the Modular Supervisor Learner (MSL) is presented that based on the known specifications and the structure of the system defines the configuration of the supervisors to learn. Then, by actively querying the simulation and interacting with the specification it explores the state-space of the system to learn a set of maximally permissive controllable supervisors

Active Learning of Modular Plant Models

Model-based techniques are these days being embraced by the industry in their development frameworks. While model-based approaches allow for offline verification and validation of the system, and have other advantages over existing methods, they do have their own challenges. One of the challenges is to obtain a model describing the behavior of the system. In this paper we present the Modular Plant Learner (MPL), an algorithm that explores the state-space and constructs a discrete model of a system. The MPL takes as input a hypothesis structure of the system - called the PSH - and using this information, interacts with a simulation of the system to construct a modular discrete-event model. Using an example we show how the algorithm uses the structural information provided - the PSH - to search the state-space in a smart manner, mitigating the state-space explosion problem

Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations

Published by Informa UK Limited, trading as Taylor & Francis Group. The technological advancements of recent years have increased the complexity of manufacturing systems, and the ongoing transformation to Industry\ua04.0 will further aggravate the situation. This is leading to a point where existing systems on the factory floor get outdated, increasing the gap between existing technologies and state-of-the-art systems, making them incompatible. This paper presents an event-based data pipeline architecture, that can be applied to legacy systems as well as new state-of-the-art systems, to collect data from the factory floor. In the presented architecture, actions executed by the resources are converted to event streams, which are then transformed into an abstraction called operations. These operations correspond to the tasks performed in the manufacturing station. A sequence of these operations recount the task performed by the station. We demonstrate the usability of the collected data by using conformance analysis to detect when the manufacturing system has deviated from its defined model. The described architecture is developed in Sequence Planner–a tool for modelling and analysing production systems–and is currently implemented at an automotive company as a pilot project

Towards Automatic Learning of Discrete-Event Models from Simulations

Model-based techniques are, these days, being embraced by the manufacturing industry in their development frameworks. While model-based approaches allow for offline verification and validation before physical commissioning, and have other advantages over existing methods, they do have their own challenges. Firstly, models are typically created manually and hence are prone to errors. Secondly, once a model is created, tested, and put into use on the factory floor, there is an added effort required to maintain and update it. This paper is a preliminary study of the feasibility of automatically obtaining formal models from virtual simulations. We apply the foundational algorithm from the active automata learning community to study the requirements and enhancements needed to be able to derive discrete event models from virtual simulations. An abstract model in the form of operations is learned by applying this algorithm on a simulation model composed of discrete operations. While a major bottleneck to be solved is the generation of counterexamples, the results seem promising to apply model learning in practice

Automatically Learning Formal Models from Autonomous Driving Software

The correctness of autonomous driving software is of utmost importance, as incorrect behavior may have catastrophic consequences. Formal model-based engineering techniques can help guarantee correctness and thereby allow the safe deployment of autonomous vehicles. However, challenges exist for widespread industrial adoption of formal methods. One of these challenges is the model construction problem. Manual construction of formal models is time-consuming, error-prone, and intractable for large systems. Automating model construction would be a big step towards widespread industrial adoption of formal methods for system development, re-engineering, and reverse engineering. This article applies active learning techniques to obtain formal models of an existing (under development) autonomous driving software module implemented in MATLAB. This demonstrates the feasibility of automated learning for automotive industrial use. Additionally, practical challenges in applying automata learning, and possible directions for integrating automata learning into the automotive software development workflow, are discussed

Real-time Visualization of Robot Operation Sequences

Evaluation of manufacturing systems requires large amounts of accurate data from the factory floor. This data is then processed to calculate Key Performance Indicators (KPIs), evaluation metrics used within the manufacturing industry by engineers and managers in order to make data-driven decisions. Mechanisms to capture large scales of usable data, which is both reliable and scalable is, more often than not, scarce. In this paper, we provide an approach to capture data from robot actions, which can be applied to both legacy and current state-of-the-art manufacturing systems. By exploiting the robot code structure, robot actions are converted to event streams that are transformed into a higher usable abstraction of data. Applicability of this data is demonstrated, primarily, by visualizations. The described approach is developed in Sequence Planner - a tool for modeling and analyzing production systems - and is currently implemented at an automotive company as a pilot project to visualize and examine what goes on on the factory floor

Attitudes towards non-invasive prenatal diagnosis among obstetricians in Pakistan: a developing, Islamic country

Objectives Stakeholders' views are essential for informing implementation strategies for non-invasive prenatal testing (NIPT). Little is known about such views in developing countries. We explored attitudes towards NIPT among obstetricians in Pakistan, a developing, Islamic country. Methods A 35-item questionnaire was distributed and collected at eight events (a national conference and seven workshops in five cities) for obstetric professionals on advances in fetal medicine. Results Responses from 113 obstetrician show positive attitudes towards implementation of NIPT: 95% agreed prevention of genetic conditions was a necessity, and 97% agreed public hospitals should provide prenatal screening tests. However, participants also agreed the availability of NIPT would increase social pressure on women to have prenatal screening tests and to terminate an affected pregnancy (53% and 63%, respectively). Most participants would not offer NIPT for sex determination (55%), although 31% would. The most valued aspects of NIPT were its safety, followed by its utility and then accuracy. Conclusion Participants generally supported the implementation of NIPT but raised concerns about social implications. Therefore, national policy is needed to regulate the implementation of NIPT, and pretest information and post-test genetic counselling are needed to mitigate social pressure and support parents to make informed decisions

Towards Automatic Generation of Formal Models for Highly Automated Manufacturing Systems

The manufacturing industry is undergoing a digital revolution, often referred to as Industry 4.0. The aim of this revolution is to transform the factories into, so called, smart factories. These smart factories will be modular, decentralized, and interconnected, to achieve higher level automation and flexibility. Additionally, a smart factory will have a digital twin, a virtual replica that allows testing, monitoring, and visualization of the factory behavior. As these factories are aimed to be completely automated, ensuring correctness and safety of the control logic in each sub-system of the factory is of utmost importance.The need for having digitalized tools that support operators and engineers was identified in a survey that was conducted to understand the problems faced during maintenance of manufacturing systems. To this end, this thesis provides an architecture that can be applied on old legacy systems as well as new state-of-the-art systems to collect data from the factory floor. The data obtained can be visualized in the form of Gantt charts to help operators keep track of the execution of the station. Furthermore, a model that captures the behavior of the system can be created by applying Process Mining algorithms to the collected data.Model-based techniques have shown to be beneficial in developing control logic for highly automated and flexible manufacturing systems, as these techniques offer tools to test and formally verify the control logic to guarantee its correctness. These formal tools operate on such a model of the behavior of the system. However, manually constructing a model on which these tools can be applied is a tedious and error prone task, seldom deemed to be worth the effort. Thus, supporting engineers to build models will improve the adoption of formal tools within the manufacturing industry.In order to obtain a formal model during the early development phase of the manufacturing system, this thesis studies the possibility to automatically infer a model of a system by interacting with its digital twin. The suggested L+ algorithm, an extension of the well-known L* algorithm, shows that it is possible to automatically build formal models in this way. Additionally, certain shortcomings are identified and need to be addressed before being able to these methods in a practical setting

Synthesis of Supervisors for Unknown Plant Models Using Active Learning

This paper proposes an approach to synthesize a discrete-event supervisor to control a plant, the behavior model of which is unknown, so as to satisfy a given specification. To this end, the $L^{*}$ algorithm is modified so that it can actively query a plant simulation and the specification to hypothesize a supervisor. The resulting hypothesis is the maximally permissive controllable supervisor from which the maximally permissive controllable and non-blocking supervisor can be extracted. The practicality of this method is demonstrated by an example