# Inductive Learning of Declarative Domain-Specific Heuristics for ASP

arXiv: https://arxiv.org/abs/2308.15863 · HTML: https://mlsanity.com/paper/2308.15863/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 30 Aug 2023.

## Builds on (most cited first, 5 of 5)

- [Multi-shot ASP solving with clingo](https://mlsanity.com/paper/1705.09811/) (2017)
- [ASP-Core-2 Input Language Format](https://mlsanity.com/paper/1911.04326/) (2019)
- [The ILASP system for Inductive Learning of Answer Set Programs](https://mlsanity.com/paper/2005.00904/) (2020)
- [Combining Answer Set Programming and Domain Heuristics for Solving Hard Industrial Problems (Application Paper)](https://mlsanity.com/paper/1608.00730/) (2016)
- [Conflict-driven Inductive Logic Programming](https://mlsanity.com/paper/2101.00058/) (2020)

## Abstract (arXiv)

Domain-specific heuristics are a crucial technique for the efficient solving of problems that are large or computationally hard. Answer Set Programming (ASP) systems support declarative specifications of domain-specific heuristics to improve solving performance. However, such heuristics must be invented manually so far. Inventing domain-specific heuristics for answer-set programs requires expertise with the domain under consideration and familiarity with ASP syntax, semantics, and solving technology. The process of inventing useful heuristics would highly profit from automatic support. This paper presents a novel approach to the automatic learning of such heuristics. We use Inductive Logic Programming (ILP) to learn declarative domain-specific heuristics from examples stemming from (near-)optimal answer sets of small but representative problem instances. Our experimental results indicate that the learned heuristics can improve solving performance and solution quality when solving larger, harder instances of the same problem.