  Generalising and re-using knowledge learned while solving one problem
instance has been neglected by state-of-the-art answer set solvers. We suggest
a new approach that generalises learned nogoods for re-use to speed-up the
solving of future problem instances. Our solution combines well-known ASP
solving techniques with deductive logic-based machine learning. Solving
performance can be improved by adding learned non-ground constraints to the
original program. We demonstrate the effects of our method by means of
realistic examples, showing that our approach requires low computational cost
to learn constraints that yield significant performance benefits in our test
cases. These benefits can be seen with ground-and-solve systems as well as
lazy-grounding systems. However, ground-and-solve systems suffer from
additional grounding overheads, induced by the additional constraints in some
cases. By means of conflict minimization, non-minimal learned constraints can
be reduced. This can result in significant reductions of grounding and solving
efforts, as our experiments show. (Under consideration for acceptance in TPLP.)
