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.

中文翻译：

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推进惰性接地 ASP 求解技术——重启、相位节省、启发式等

答案集编程 (ASP) 是一种强大且富有表现力的知识表示范式，在基于逻辑的 AI 中具有大量应用。然而，传统的 ground-and-solve 方法需要 ASP 程序预先接地，因此存在所谓的接地瓶颈（即，ASP 程序很容易耗尽所有可用内存，从而变得无法解决）。作为补救措施，已经开发了惰性接地 ASP 求解器，但许多用于接地 ASP 求解的最先进技术尚未可供他们使用。在这项工作中，我们首次展示了对许多重要技术的惰性基础设置的适应，例如重新启动、阶段保存、域独立启发式和学习子句删除。此外，