  We define the problem of learning a transducer ${S}$ from a target language
$U$ containing possibly conflicting transducers, using membership queries and
conjecture queries. The requirement is that the language of ${S}$ be a subset
of $U$. We argue that this is a natural question in many situations in hardware
and software verification. We devise a learning algorithm for this problem and
show that its time and query complexity is polynomial with respect to the rank
of the target language, its incompatibility measure, and the maximal length of
a given counterexample. We report on experiments conducted with a prototype
implementation.
