It has been a long time since I wrote here. The last few weeks have been quite hectic with all the thesis work. Now that it is written and submitted, I hope I can be more punctual with these mails :)
A short description of my work: Ensuring the correctness of automated systems is crucial. The supervisory control theory proposes techniques to help build control solutions that provide certain correctness guarantees. These techniques rely on a model describing the behavior of the system. Unfortunately, such models are hard to create, thus limiting the industrial adoption of SCT. This thesis aims to improve the situation by providing an approach to automatically learn a model that captures the system's behavior.
To this end, we propose two approaches to integrate active learning and supervisory control theory. Active learning is a promising technique to learn models by interacting with the system to be learned. Using active learning helps avoid the manual step of creating models, thus allowing the use of supervisory control techniques in the absence of models.
The presented approaches are implemented in a tool MIDES. Two case studies have been undertaken to understand the industrial challenges of the proposed approaches. In the first, the applicability in a manufacturing scenario is studied. In the second, a model of a software component in a self-driving car was learned. Both studies highlight the benefits of the proposed methods while also pointing out their limitations.
And, here is the invite for those of you who wish to attend, here are the official details.