{"type":"ScholarlyArticle","title":"Lifting Symmetry Breaking Constraints with Inductive Logic Programming","authors":[{"name":"Alice Tarzariol","slug":"alice-tarzariol","given":"Alice","family":"Tarzariol","affiliation":""},{"name":"Martin Gebser","slug":"martin-gebser","given":"Martin","family":"Gebser","affiliation":""},{"name":"Konstantin Schekotihin","slug":"konstantin-schekotihin","given":"Konstantin","family":"Schekotihin","affiliation":""}],"abstract":"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. Moreover, the computed SBCs are propositional and, therefore, can neither be meaningfully interpreted nor transferred to other instances.\r\nTo 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. \r\nExperiments 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.","doi":"10.24963/ijcai.2021/284","url":"https://ijcai.org/proceedings/2021/284","pdf":"https://ijcai.org/proceedings/2021/0284.pdf","markdown":"https://ijcai.org/proceedings/2021/284.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/284","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":284,"pages":{"first":2062,"last":2068},"keywords":["Machine Learning: Explainable/Interpretable Machine Learning","Knowledge Representation and Reasoning: Leveraging Knowledge and Learning","Constraints and SAT: Constraints: Modeling, Solvers, Applications"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}