Automated Hybrid Grounding Using Structural and Data-Driven Heuristics

TL;DR

This paper presents an automated hybrid grounding method for Answer Set Programming that intelligently chooses between body-decoupled and standard grounding using data-structural heuristics, improving performance on challenging instances.

Contribution

It introduces a splitting algorithm that automates the decision of grounding strategy based on rule structure and data, advancing hybrid grounding techniques.

Findings

Improved grounding performance on hard-to-ground scenarios.

Approaches state-of-the-art results on hard-to-solve instances.

Demonstrates effectiveness of data-structural heuristics in grounding decisions.

Abstract

The grounding bottleneck poses one of the key challenges that hinders the widespread adoption of Answer Set Programming in industry. Hybrid Grounding is a step in alleviating the bottleneck by combining the strength of standard bottom-up grounding with recently proposed techniques where rule bodies are decoupled during grounding. However, it has remained unclear when hybrid grounding shall use body-decoupled grounding and when to use standard bottom-up grounding. In this paper, we address this issue by developing automated hybrid grounding: we introduce a splitting algorithm based on data-structural heuristics that detects when to use body-decoupled grounding and when standard grounding is beneficial. We base our heuristics on the structure of rules and an estimation procedure that incorporates the data of the instance. The experiments conducted on our prototypical implementation demonstrate promising results, which show an improvement on hard-to-ground scenarios, whereas on hard-to-solve instances we approach state-of-the-art performance.