  Active learning of timed languages is concerned with the inference of timed
automata from observed timed words. The agent can query for the membership of
words in the target language, or propose a candidate model and verify its
equivalence to the target. The major difficulty of this framework is the
inference of clock resets, central to the dynamics of timed automata, but not
directly observable. Interesting first steps have already been made by
restricting to the subclass of event-recording automata, where clock resets are
tied to observations. In order to advance towards learning of general timed
automata, we generalize this method to a new class, called reset-free
event-recording automata, where some transitions may reset no clocks. This
offers the same challenges as generic timed automata while keeping the simpler
framework of event-recording automata for the sake of readability. Central to
our contribution is the notion of invalidity, and the algorithm and data
structures to deal with it, allowing on-the-fly detection and pruning of reset
hypotheses that contradict observations, a key to any efficient active-learning
procedure for generic timed automata.
