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

TL;DR

This paper enhances lazy-grounding ASP solvers by adapting key techniques like restarts and heuristics, significantly improving their solving capabilities while highlighting the need for portfolio approaches.

Contribution

It introduces the first adaptations of important solving techniques to lazy-grounding ASP, demonstrating their impact on solver performance.

Findings

Large improvements in solving capabilities observed.

Negative effects identified in certain cases.

Highlights the importance of portfolio solving strategies.

Abstract

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.