Numerous systems operate in areas like healthcare, transportation, finance, or robotics. They interact with our everyday life, and thus strong reliability or safety guarantees on the control of these systems are required. However, traditional methods to ensure such guarantees, such as from the areas of formal verification, control theory, or testing, often do not account for several fundamental aspects adequately. For instance, the uncertainty that is inherent to systems that operate with data from the real world; the uncertainty coming from the systems themselves being only partially known/black box; and the sheer complexity or the astronomical size of the systems.

Therefore, a joining of forces is in order for the areas of verification, machine learning, AI planning, and general statistical methods. Within this track, we welcome all contributions that may be placed on the interface of these areas. Examples for concrete topics are:

  • safety in reinforcement learning
  • verification of probabilistic systems with the help of learning
  • statistical guarantees on system correctness, statistical model checking
  • testing and model learning under uncertainty (‘flaky’ testing)

Program 🔗

Submission 🔗

Please submit your contributions via EquinOCS

Track Organizers 🔗

NameInstitution
Jan KřetínskýTU Munich, DE
Kim LarsenAalborg University, DK
Nils JansenRadboud University Nijmegen, NL
Bettina KönighoferTU Graz, AT