@TechReport{ it:2008-013,
author = {Olga Grinchtein and Bengt Jonsson and Martin Leucker},
title = {Learning of Event-Recording Automata},
institution = {Department of Information Technology, Uppsala University},
department = {Division of Computer Systems},
year = {2008},
number = {2008-013},
month = apr,
abstract = {In regular inference, a regular language is inferred from
answers to a finite set of membership queries, each of
which asks whether the language contains a certain word.
One of the most well-known regular inference algorithms is
the $L^*$ algorithm due to Dana Angluin. However, there are
almost no extensions of these algorithms to the setting of
timed systems. We extend Angluin's algorithm for on-line
learning of regular languages to the setting of timed
systems. Since timed automata can freely use an arbitrary
number of clocks, we restrict our attention to systems that
can be described by deterministic event-recording automata
(DERAs). We present three algorithms, $TL_sg^*$, $TL_nsg^*$
and $TL_s^*$, for inference of DERAs. In $TL_sg^*$ and
$TL_nsg^*$, we further restrict event-recording automata to
be event-deterministic in the sense that each state has at
most one outgoing transition per action; learning such an
automaton becomes significantly more tractable. The
algorithm $TL_nsg^*$ builds on $TL_sg^*$, by attempts to
construct a smaller (in number of locations) automaton.
Finally, $TL_s^*$ is a learning algorithm for a full class
of deterministic event-recording automata, which infers a
so called \emph{simple} DERA, which is similar in spirit to
the region graph.}
}