Towards Comprehensible Representation of Controllers using Machine Learning

Abstract

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.