  We extend the Kearns-Vazirani learning algorithm to be able to handle systems
that change over time. We present a new learning algorithm that can reuse and
update previously learned behavior, implement it in the LearnLib library, and
evaluate it on large examples, to which we make small adjustments between two
runs of the algorithm. In these experiments our algorithm significantly
outperforms both the classic Kearns-Vazirani learning algorithm and the current
state-of-the-art adaptive algorithm.
