The grounding bottleneck poses one of the key challenges that hinders the widespread adoption of Answer Set Programming in industry. Hybrid Grounding is a step in alleviating the bottleneck by combining the strength of standard bottom-up grounding with recently proposed techniques where rule bodies are decoupled during grounding. However, it has remained unclear when hybrid grounding shall use body-decoupled grounding and when to use standard bottom-up grounding. In this paper, we address this issue by developing automated hybrid grounding: we introduce a splitting algorithm based on data-structural heuristics that detects when to use body-decoupled grounding and when standard grounding is beneficial. We base our heuristics on the structure of rules and an estimation procedure that incorporates the data of the instance. The experiments conducted on our prototypical implementation demonstrate promising results, which show an improvement on hard-to-ground scenarios, whereas on hard-to-solve instances we approach state-of-the-art performance.

翻译：基化瓶颈是阻碍答案集编程在工业界广泛应用的关键挑战之一。混合基化通过将标准自底向上基化的优势与最近提出的规则体在基化过程中解耦的技术相结合，为缓解该瓶颈迈出了一步。然而，何时应采用体解耦基化、何时应采用标准自底向上基化，这一问题尚未明确。本文通过开发自动化混合基化来解决该问题：我们引入一种基于数据-结构启发式的分割算法，该算法能够检测何时使用体解耦基化以及何时标准基化更为有利。我们的启发式方法基于规则结构，并结合了实例数据估计程序。在原型实现上进行的实验显示出有前景的结果：该方法在难基化场景中表现出改进，而在难求解实例上则接近现有最优性能。