Tehran Institute for Advanced Studies (TEIAS)

/ Active Learning of Decomposable Systems


Active Learning of Decomposable Systems

November 11, 2020
(21 Aban, 1399)


This Talk is online


Jan Friso Groote

Professor of Computer Science at the Eindhoven University of Technology


Active automata learning is a technique of querying black box systems and modelling their behavior. In this paper, we aim to apply active learning in parts. We formalize the conditions on systems with a decomposable set of actions that make learning in parts possible. The systems are themselves decomposable through non-intersecting subsets of actions. Learning these subsystems/ components requires less time and resources. We prove that the technique works for both two components as well as an arbitrary number of components. We illustrate the usefulness of this technique through a classical example and through a real example from the industry.


Jan Friso Groote  is a Full Professor and Chair of Formal Systems Analysis group in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). His areas of expertise include Computer systems, architectures, software, algorithms, embedded systems and formal methods. He is the founding father of the process modeling language and analysis tool set  mCRL2. His current research goal is to show that formal analysis techniques can be used to design the software for complete systems. For this it is not only necessary to improve the verification techniques and algorithms, but it is also important to develop software development styles suitable for verification. Industrial experience shows that this reduces the development time with a factor three increasing the quality with a factor 10. Especially regarding the quality, it can be expected that substantial further improvements are possible, hopefully leading to zero defect software.