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Communication Dans Un Congrès Année : 2022

TAG: Learning Timed Automata from Logs

Résumé

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.
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Dates et versions

hal-03564455 , version 1 (11-02-2022)

Identifiants

  • HAL Id : hal-03564455 , version 1

Citer

Lénaïg Cornanguer, Christine Largouët, Laurence Rozé, Alexandre Termier. TAG: Learning Timed Automata from Logs. AAAI 2022 - 36th AAAI Conference on Artificial Intelligence, Feb 2022, Virtual, Canada. pp.1-9. ⟨hal-03564455⟩
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