  Answer-Set Programming (ASP) is a powerful and expressive knowledge
representation paradigm with a significant number of applications in
logic-based AI. The traditional ground-and-solve approach, however, requires
ASP programs to be grounded upfront and thus suffers from the so-called
grounding bottleneck (i.e., ASP programs easily exhaust all available memory
and thus become unsolvable). As a remedy, lazy-grounding ASP solvers have been
developed, but many state-of-the-art techniques for grounded ASP solving have
not been available to them yet. In this work we present, for the first time,
adaptions to the lazy-grounding setting for many important techniques, like
restarts, phase saving, domain-independent heuristics, and learned-clause
deletion. Furthermore, we investigate their effects and in general observe a
large improvement in solving capabilities and also uncover negative effects in
certain cases, indicating the need for portfolio solving as known from other
solvers. Under consideration for acceptance in TPLP.
