# Conflict-driven Inductive Logic Programming

arXiv: https://arxiv.org/abs/2101.00058 · HTML: https://mlsanity.com/paper/2101.00058/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [Lifting Symmetry Breaking Constraints with Inductive Logic Programming](https://mlsanity.com/paper/2112.11806/) (2021)
- [Efficient lifting of symmetry breaking constraints for complex combinatorial problems](https://mlsanity.com/paper/2205.07129/) (2022)
- [Inductive Learning of Robot Task Knowledge from Raw Data and Online Expert Feedback](https://mlsanity.com/paper/2501.07507/) (2025)
- [Learning Symbolic Persistent Macro-Actions for POMDP Solving Over Time](https://mlsanity.com/paper/2505.03668/) (2025)
- [A Model-Oriented Approach for Lifting Symmetries in Answer Set Programming](https://mlsanity.com/paper/2208.03095/) (2022)
- [Inductive Learning of Declarative Domain-Specific Heuristics for ASP](https://mlsanity.com/paper/2308.15863/) (2023)
- [LearnAD: Learning Interpretable Rules for Brain Networks in Alzheimer's Disease Classification](https://mlsanity.com/paper/2601.00877/) (2025)
- [Streamliners for Answer Set Programming](https://mlsanity.com/paper/2604.19251/) (2026)

## Abstract (arXiv)

The goal of Inductive Logic Programming (ILP) is to learn a program that explains a set of examples. Until recently, most research on ILP targeted learning Prolog programs. The ILASP system instead learns Answer Set Programs (ASP). Learning such expressive programs widens the applicability of ILP considerably; for example, enabling preference learning, learning common-sense knowledge, including defaults and exceptions, and learning non-deterministic theories. Early versions of ILASP can be considered meta-level ILP approaches, which encode a learning task as a logic program and delegate the search to an ASP solver. More recently, ILASP has shifted towards a new method, inspired by conflict-driven SAT and ASP solvers. The fundamental idea of the approach, called Conflict-driven ILP (CDILP), is to iteratively interleave the search for a hypothesis with the generation of constraints which explain why the current hypothesis does not cover a particular example. These coverage constraints allow ILASP to rule out not just the current hypothesis, but an entire class of hypotheses that do not satisfy the coverage constraint. This paper formalises the CDILP approach and presents the ILASP3 and ILASP4 systems for CDILP, which are demonstrated to be more scalable than previous ILASP systems, particularly in the presence of noise. Under consideration in Theory and Practice of Logic Programming (TPLP).