Fast Inference for Probabilistic Answer Set Programs via the Residual Program

TL;DR

This paper introduces a method to accelerate probabilistic answer set programming by leveraging residual programs that exclude irrelevant parts, resulting in faster inference on graph datasets.

Contribution

It proposes using residual programs derived from SLG resolution to improve inference efficiency in probabilistic answer set programming.

Findings

Significantly faster inference on graph datasets.

Residual programs reduce grounding size and computation time.

Approach maintains correctness of probabilistic inference.

Abstract

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.