一句话：用机器学习在保留领域知识的前提下，简洁表示模型检测器合成的控制器，并配套可视化工具。
论文关注信息物理系统等真实系统在部署前对安全且最优控制器的需求。给定系统的数学模型与规格，模型检测器可以自动合成控制器，但合成结果往往难以被软件工程师理解，从而阻碍调试与监控。作者提出新方法，利用机器学习将模型检测器合成的控制器表示为简洁、易读的形式，并在表示中融入系统自身的领域知识，以保持其语义相关性。论文还计划实现一个可视化工具，并与现有模型检测器集成，使工程师能更高效地调试和监控系统。该工作为研究构想，摘要中未报告实验结果或评估结论。
From the point of view of a software engineer, having safe and optimal controllers for real life systems like cyber physical systems is a crucial requirement before deployment. Given the mathematical model of these systems along with their specifications, model checkers can be used to synthesize controllers for them. The given work proposes novel approaches for making controller analysis easier by using machine learning to represent the controllers synthesized by model checkers in a succinct manner, while also incorporating the domain knowledge of the system. It also proposes the implementation of a visualization tool which will be integrated into existing model checkers. A lucid controller representation along with a tool to visualize it will help the software engineer debug and monitor the system much more efficiently.
DOI 原文 ·
@inproceedings{paperbot5254,
title = {Towards Comprehensible Representation of Controllers using Machine Learning},
author = {Gargi Balasubramaniam},
booktitle = {ASE},
year = {2019},
doi = {10.1109/ase.2019.00163}
}