Efficient Lifting of Symmetry Breaking Constraints for Complex Combinatorial Problems

Alice Tarzariol, Konstantin Schekotihin, Martin Gebser, Mark Law

Research output: Contribution to journalArticlepeer-review


Many industrial applications require finding solutions to challenging combinatorial problems. Efficient elimination of symmetric solution candidates is one of the key enablers for high-performance solving. However, existing model-based approaches for symmetry breaking are limited to problems for which a set of representative and easily solvable instances is available, which is often not the case in practical applications. This work extends the learning framework and implementation of a model-based approach for Answer Set Programming to overcome these limitations and address challenging problems, such as the Partner Units Problem. In particular, we incorporate a new conflict analysis algorithm in the Inductive Logic Programming system ILASP, redefine the learning task, and suggest a new example generation method to scale up the approach. The experiments conducted for different kinds of Partner Units Problem instances demonstrate the applicability of our approach and the computational benefits due to the first-order constraints learned.

Original languageEnglish
Pages (from-to)606-622
Number of pages17
JournalTheory and Practice of Logic Programming
Issue number4
Publication statusPublished - 30 Jul 2022


  • answer set programming
  • inductive logic programming
  • symmetry breaking constraints

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computational Theory and Mathematics
  • Artificial Intelligence


Dive into the research topics of 'Efficient Lifting of Symmetry Breaking Constraints for Complex Combinatorial Problems'. Together they form a unique fingerprint.

Cite this