Advancing Lazy-Grounding ASP Solving Techniques -- Restarts, Phase Saving, Heuristics, and More
This work addresses the memory and scalability issues for users of logic-based AI systems, but it is incremental as it adapts existing techniques to a new setting.
The paper tackled the grounding bottleneck in Answer-Set Programming by adapting techniques like restarts and heuristics to lazy-grounding solvers, resulting in large improvements in solving capabilities, though with some negative effects in certain cases.
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.