Automated Hybrid Grounding Using Structural and Data-Driven Heuristics

2025-07-23

AI Summary

Answer Set Programming (ASP) faces a grounding bottleneck in industrial applications due to large-scale instances, where conventional grounding techniques become computationally prohibitive. Method: This paper proposes a hybrid grounding framework that automatically selects between body-decoupled grounding and standard bottom-up grounding based on instance characteristics. The core of the approach is a novel splitting algorithm that jointly analyzes rule structure and estimates instance data statistics, enabling dynamic, structure-aware, and data-driven heuristic selection of grounding strategies. Contribution/Results: Experimental evaluation demonstrates that the method significantly outperforms traditional grounders on hard-to-ground instances and achieves performance competitive with state-of-the-art ASP solvers on challenging benchmarks. By adaptively choosing grounding modes, the framework enhances grounding adaptability, robustness, and scalability—addressing critical limitations in real-world ASP deployment.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Automating hybrid grounding for Answer Set Programming
• Deciding when to use body-decoupled vs standard grounding
• Improving performance on hard-to-ground scenarios

Innovation

• Automated hybrid grounding with structural heuristics
• Splitting algorithm for body-decoupled grounding
• Data-driven estimation for optimal grounding selection