# Lifting Symmetry Breaking Constraints with Inductive Logic Programming

arXiv: https://arxiv.org/abs/2112.11806 · HTML: https://mlsanity.com/paper/2112.11806/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 38 references that are in the map.
- First posted on arXiv 22 Dec 2021.

## Builds on (most cited first, 8 of 8)

- [Inductive logic programming at 30: a new introduction](https://mlsanity.com/paper/2008.07912/) (2020)
- [ASP-Core-2 Input Language Format](https://mlsanity.com/paper/1911.04326/) (2019)
- [Turning 30: New Ideas in Inductive Logic Programming](https://mlsanity.com/paper/2002.11002/) (2020)
- [The ILASP system for Inductive Learning of Answer Set Programs](https://mlsanity.com/paper/2005.00904/) (2020)
- [Inductive Learning of Answer Set Programs from Noisy Examples](https://mlsanity.com/paper/1808.08441/) (2018)
- [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)
- [On Local Domain Symmetry for Model Expansion](https://mlsanity.com/paper/1608.02688/) (2016)

## Built on by (most cited first, 4 of 4)

- [Efficient lifting of symmetry breaking constraints for complex combinatorial problems](https://mlsanity.com/paper/2205.07129/) (2022)
- [A Model-Oriented Approach for Lifting Symmetries in Answer Set Programming](https://mlsanity.com/paper/2208.03095/) (2022)
- [Symmetry breaking for inductive logic programming](https://mlsanity.com/paper/2508.06263/) (2025)
- [Streamliners for Answer Set Programming](https://mlsanity.com/paper/2604.19251/) (2026)

## Abstract (arXiv)

Efficient omission of symmetric solution candidates is essential for combinatorial problem-solving. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constraints (SBCs) for each given problem instance. However, the application of such approaches to large-scale instances or advanced problem encodings might be problematic since the computed SBCs are propositional and, therefore, can neither be meaningfully interpreted nor transferred to other instances. As a result, a time-consuming recomputation of SBCs must be done before every invocation of a solver. To overcome these limitations, we introduce a new model-oriented approach for Answer Set Programming that lifts the SBCs of small problem instances into a set of interpretable first-order constraints using the Inductive Logic Programming paradigm. Experiments demonstrate the ability of our framework to learn general constraints from instance-specific SBCs for a collection of combinatorial problems. The obtained results indicate that our approach significantly outperforms a state-of-the-art instance-specific method as well as the direct application of a solver.