TAG: Learning Timed Automata from Logs


  • Lénaïg Cornanguer Inria Univ Rennes CNRS IRISA
  • Christine Largouët Institut Agro Univ Rennes Inria CNRS IRISA
  • Laurence Rozé Univ Rennes INSA Rennes CNRS Inria IRISA
  • Alexandre Termier Univ Rennes Inria CNRS IRISA




Data Mining & Knowledge Management (DMKM)


Event logs are often one of the main sources of information to understand the behavior of a system. While numerous approaches have extracted partial information from event logs, in this work, we aim at inferring a global model of a system from its event logs. We consider real-time systems, which can be modeled with Timed Automata: our approach is thus a Timed Automata learner. There is a handful of related work, however, they might require a lot of parameters or produce Timed Automata that either are undeterministic or lack precision. In contrast, our proposed approach, called TAG, requires only one parameter and learns a deterministic Timed Automaton having a good tradeoff between accuracy and complexity of the automata. This allows getting an interpretable and accurate global model of the real-time system considered. Our experiments compare our approach to the related work and demonstrate its merits.




How to Cite

Cornanguer, L., Largouët, C., Rozé, L., & Termier, A. (2022). TAG: Learning Timed Automata from Logs. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), 3949-3958. https://doi.org/10.1609/aaai.v36i4.20311



AAAI Technical Track on Data Mining and Knowledge Management