# Heuristic Based Induction of Answer Set Programs: From Default theories to combinatorial problems

arXiv: https://arxiv.org/abs/1802.06462 · HTML: https://mlsanity.com/paper/1802.06462/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2018.

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

- [FOLD-R++: A Scalable Toolset for Automated Inductive Learning of Default Theories from Mixed Data](https://mlsanity.com/paper/2110.07843/) (2021)
- [FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data](https://mlsanity.com/paper/2202.06913/) (2022)
- [FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank](https://mlsanity.com/paper/2206.07295/) (2022)
- [Inductive Learning for Possibilistic Logic Programs Under Stable Models](https://mlsanity.com/paper/2510.07069/) (2025)

## Abstract (arXiv)

Significant research has been conducted in recent years to extend Inductive Logic Programming (ILP) methods to induce Answer Set Programs (ASP). These methods perform an exhaustive search for the correct hypothesis by encoding an ILP problem instance as an ASP program. Exhaustive search, however, results in loss of scalability. In addition, the language bias employed in these methods is overly restrictive too. In this paper we extend our previous work on learning stratified answer set programs that have a single stable model to learning arbitrary (i.e., non-stratified) ones with multiple stable models. Our extended algorithm is a greedy FOIL-like algorithm, capable of inducing non-monotonic logic programs, examples of which includes programs for combinatorial problems such as graph-coloring and N-queens. To the best of our knowledge, this is the first heuristic-based ILP algorithm to induce answer set programs with multiple stable models.