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X-WR-CALNAME:[MTV] Gaëtan Staquet (IRISA) - Active Learning of Mealy Machin
 es with Timers
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DESCRIPTION:In order to understand and verify complex systems\, we need acc
 urate models that are either understandable for humans or can be analyzed 
 fully automatically. Such models\, however\, are typically not available f
 or legacy software. Active automata learning is a black-box technique for 
 constructing state machine models of software and hardware components from
  information obtained through testing (i.e.\, providing inputs and observi
 ng the resulting outputs) and may thus fill this gap. In many applications
 \, timing plays a crucial role\, which in turn makes extending automata le
 arning to a setting that incorporates quantitative timing information chal
 lenging. In this talk\, we present an active learning algorithm for a gene
 ral class of Mealy machines with timers (MMTs) in a black-box context. A M
 ealy machine is a finite state machine that outputs a sequence of symbols 
 for every processed input word. We then augment it with timers that force 
 certain transitions to occur after a certain amount of time has elapsed. O
 ur algorithm is an extension of the L# algorithm of Vaandrager et al. [1] 
 to a timed setting. Like the algorithm for learning timed automata propose
 d by Waga [2]\, our algorithm is inspired by ideas of Maler & Pnueli [3]. 
 Based on the elementary languages of [3]\, both Waga's and our algorithm u
 se symbolic queries\, which are then implemented using finitely many concr
 ete queries. However\, whereas Waga needs exponentially many concrete quer
 ies to implement a single symbolic query\, we only need a polynomial numbe
 r. This is because in order to learn a timed automaton\, a learner needs t
 o determine the exact guard and reset for each transition (out of exponent
 ially many possibilities)\, whereas for learning an MMT a learner only nee
 ds to figure out which of the preceding transitions caused a timeout. As s
 hown in a previous work [4]\, this can be done efficiently for a subclass 
 of MMTs that are “race-avoiding”: if a timeout is caused by a preceding in
 put then a slight change in the timing of this input will induce a corresp
 onding change in the timing of the timeout.\n\n[1]: Frits W. Vaandrager\, 
 Bharat Garhewal\, Jurriaan Rot\, and Thorsten Wißmann. A new approach for 
 active automata learning based on apartness. TACAS 2022.\n[2]: Masaki Waga
 . Active learning of deterministic timed automata with myhill-nerode style
  characterization. CAV 2023.\n[3]: Oded Maler and Amir Pnueli. On recogniz
 able timed languages. FOSSACS 2004.\n[4]: Véronique Bruyère\, Guillermo A.
  Pérez\, Gaëtan Staquet\, and Frits W. Vaandrager. Automata with timers. F
 ORMATS 2023.\n\n\nImport automatique depuis https://framagenda.org/remote.
 php/dav/public-calendars/D3yqF7nwys9amfyY?export par sync_icals_to_drupal.
 py pour M2F
DTSTART;TZID=Europe/Paris:20250206T130000
DTEND;TZID=Europe/Paris:20250206T140000
LOCATION:076
SEQUENCE:0
SUMMARY:[MTV] Gaëtan Staquet (IRISA) - Active Learning of Mealy Machines wi
 th Timers
TRANSP:OPAQUE
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