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

Answer-Set Programming (ASP) is a powerful and expressive knowledge\nrepresentation paradigm with a significant number of applications in\nlogic-based AI. The traditional ground-and-solve approach, however, requires\nASP programs to be grounded upfront and thus suffers from the so-called\ngrounding bottleneck (i.e., ASP programs easily exhaust all available memory\nand thus become unsolvable). As a remedy, lazy-grounding ASP solvers have been\ndeveloped, but many state-of-the-art techniques for grounded ASP solving have\nnot been available to them yet. In this work we present, for the first time,\nadaptions to the lazy-grounding setting for many important techniques, like\nrestarts, phase saving, domain-independent heuristics, and learned-clause\ndeletion. Furthermore, we investigate their effects and in general observe a\nlarge improvement in solving capabilities and also uncover negative effects in\ncertain cases, indicating the need for portfolio solving as known from other\nsolvers. Under consideration for acceptance in TPLP.\n