# plasp 3: Towards Effective ASP Planning

arXiv: https://arxiv.org/abs/1812.04491 · HTML: https://mlsanity.com/paper/1812.04491/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2018.

## Built on by (most cited first, 6 of 6)

- [Answer Set Planning: A Survey](https://mlsanity.com/paper/2202.05793/) (2022)
- [Logic programming for deliberative robotic task planning](https://mlsanity.com/paper/2301.07550/) (2023)
- [Determining ActionReversibility in STRIPS Using Answer Set and Epistemic Logic Programming](https://mlsanity.com/paper/2108.05428/) (2021)
- [Planning as Theorem Proving with Heuristics](https://mlsanity.com/paper/2303.13638/) (2023)
- [On the generalization of learned constraints for ASP solving in temporal domains](https://mlsanity.com/paper/2401.16124/) (2024)
- [Parametric Modular Answer Set Programs Made Declarative](https://mlsanity.com/paper/2605.22716/) (2026)

## Abstract (arXiv)

We describe the new version of the PDDL-to-ASP translator plasp. First, it widens the range of accepted PDDL features. Second, it contains novel planning encodings, some inspired by SAT planning and others exploiting ASP features such as well-foundedness. All of them are designed for handling multivalued fluents in order to capture both PDDL as well as SAS planning formats. Third, enabled by multishot ASP solving, it offers advanced planning algorithms also borrowed from SAT planning. As a result, plasp provides us with an ASP-based framework for studying a variety of planning techniques in a uniform setting. Finally, we demonstrate in an empirical analysis that these techniques have a significant impact on the performance of ASP planning.