  Hard optimisation problems such as Boolean Satisfiability typically have long
solving times and can usually be solved by many algorithms, although the
performance can vary widely in practice. Research has shown that no single
algorithm outperforms all the others; thus, it is crucial to select the best
algorithm for a given problem. Supervised machine learning models can
accurately predict which solver is best for a given problem, but they require
first to run every solver in the portfolio for all examples available to create
labelled data. As this approach cannot scale, we developed an active learning
framework that addresses this problem by constructing an optimal training set,
so that the learner can achieve higher or equal performances with less training
data. Our work proves that active learning is beneficial for algorithm
selection techniques and provides practical guidance to incorporate into
existing systems.
