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

arXiv: https://arxiv.org/abs/2008.03526 · HTML: https://mlsanity.com/paper/2008.03526/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2020.

## Built on by (most cited first, 10 of 10)

- [On the Foundations of Grounding in Answer Set Programming](https://mlsanity.com/paper/2108.04769/) (2021)
- [Aggregate Semantics for Propositional Answer Set Programs](https://mlsanity.com/paper/2109.08662/) (2021)
- [On the Configuration of More and Less Expressive Logic Programs](https://mlsanity.com/paper/2203.01024/) (2022)
- [Current and Future Challenges in Knowledge Representation and Reasoning](https://mlsanity.com/paper/2308.04161/) (2023)
- [On the generalization of learned constraints for ASP solving in temporal domains](https://mlsanity.com/paper/2401.16124/) (2024)
- [Epistemic Logic Programs: Non-Ground and Counting Complexity](https://mlsanity.com/paper/2503.04731/) (2025)
- [Finite Groundings for ASP with Functions: A Journey through Consistency](https://mlsanity.com/paper/2405.15794/) (2024)
- [Automated Hybrid Grounding Using Structural and Data-Driven Heuristics](https://mlsanity.com/paper/2507.17493/) (2025)
- [Diminution: On Reducing the Size of Grounding ASP Programs](https://mlsanity.com/paper/2508.08633/) (2025)
- [Grounding Rule-Based Argumentation Using Datalog](https://mlsanity.com/paper/2508.10976/) (2025)

## Abstract (arXiv)

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.