# Fast Inference for Probabilistic Answer Set Programs via the Residual Program

arXiv: https://arxiv.org/abs/2408.07524 · HTML: https://mlsanity.com/paper/2408.07524/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 14 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Constraint Answer Set Programming without Grounding](https://mlsanity.com/paper/1804.11162/) (2018)
- [Computing Stable Models of Normal Logic Programs Without Grounding](https://mlsanity.com/paper/1709.00501/) (2017)
- [Efficient Knowledge Compilation Beyond Weighted Model Counting](https://mlsanity.com/paper/2205.07496/) (2022)
- [smProbLog: Stable Model Semantics in ProbLog for Probabilistic Argumentation](https://mlsanity.com/paper/2304.00879/) (2023)

## Abstract (arXiv)

When we want to compute the probability of a query from a Probabilistic Answer Set Program, some parts of a program may not influence the probability of a query, but they impact on the size of the grounding. Identifying and removing them is crucial to speed up the computation. Algorithms for SLG resolution offer the possibility of returning the residual program which can be used for computing answer sets for normal programs that do have a total well-founded model. The residual program does not contain the parts of the program that do not influence the probability. In this paper, we propose to exploit the residual program for performing inference. Empirical results on graph datasets show that the approach leads to significantly faster inference.