Marking Data-Informativity and Data-Driven Supervisory Control of Discrete-Event Systems
Abstract
In this paper we develop a data-driven approach for marking nonblocking supervisory control of discrete-event systems (DES). We consider a setup in which models of DES to be controlled are unknown, but a set of data concerning the behaviors of DES is available. We ask the question: Under what conditions of the available data set can a valid marking noblocking supervisor be designed for the unknown DES to satisfy a given specification? Answering this question, we identify and formalize a novel concept called marking data-informativity. Moreover, we design an algorithm for the verification of this concept. Next, if the data set fails to be marking informative, we propose two related new concepts of restricted marking data-informativity and marking informatizability. Finally, we develop an algorithm to compute the largest subset of control specification for which the data set is least restricted marking informative.
Cite
@article{arxiv.2603.05508,
title = {Marking Data-Informativity and Data-Driven Supervisory Control of Discrete-Event Systems},
author = {Yingying Liu and Kuma Fuchiwaki and Kai Cai},
journal= {arXiv preprint arXiv:2603.05508},
year = {2026}
}