  We present Self-Driven Strategy Learning ($\textit{sdsl}$), a lightweight
online learning methodology for automated reasoning tasks that involve solving
a set of related problems. $\textit{sdsl}$ does not require offline training,
but instead automatically constructs a dataset while solving earlier problems.
It fits a machine learning model to this data which is then used to adjust the
solving strategy for later problems. We formally define the approach as a set
of abstract transition rules. We describe a concrete instance of the sdsl
calculus which uses conditional sampling for generating data and random forests
as the underlying machine learning model. We implement the approach on top of
the Kissat solver and show that the combination of Kissat+$\textit{sdsl}$
certifies larger bounds and finds more counter-examples than other
state-of-the-art bounded model checking approaches on benchmarks obtained from
the latest Hardware Model Checking Competition.
