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Answer-Set Programming (ASP) is a powerful and expressive knowledgerepresentation paradigm with a significant number of applications inlogic-based AI. The traditional ground-and-solve approach, however, requiresASP programs to be grounded upfront and thus suffers from the so-calledgrounding bottleneck (i.e., ASP programs easily exhaust all available memoryand thus become unsolvable). As a remedy, lazy-grounding ASP solvers have beendeveloped, but many state-of-the-art techniques for grounded ASP solving havenot been available to them yet. In this work we present, for the first time,adaptions to the lazy-grounding setting for many important techniques, likerestarts, phase saving, domain-independent heuristics, and learned-clausedeletion. Furthermore, we investigate their effects and in general observe alarge improvement in solving capabilities and also uncover negative effects incertain cases, indicating the need for portfolio solving as known from othersolvers. Under consideration for acceptance in TPLP.

Advancing Lazy-Grounding ASP Solving Techniques -- Restarts, Phase Saving, Heuristics, and More