TY - JOUR
T1 - Efficient Lifting of Symmetry Breaking Constraints for Complex Combinatorial Problems
AU - Tarzariol, Alice
AU - Schekotihin, Konstantin
AU - Gebser, Martin
AU - Law, Mark
N1 - Funding Information:
This work was partially funded by KWF project 28472, cms electronics GmbH, FunderMax GmbH, Hirsch Armbänder GmbH, incubed IT GmbH, Infineon Technologies Austria AG, Isovolta AG, Kostwein Holding GmbH, and Privatstiftung Kärntner Sparkasse. We thank the anonymous reviewers for their valuable suggestions and comments.
Publisher Copyright:
© 2022 Cambridge University Press. All rights reserved.
PY - 2022/7/30
Y1 - 2022/7/30
N2 - 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.
AB - 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.
KW - answer set programming
KW - inductive logic programming
KW - symmetry breaking constraints
UR - http://www.scopus.com/inward/record.url?scp=85136315887&partnerID=8YFLogxK
U2 - 10.1017/S1471068422000151
DO - 10.1017/S1471068422000151
M3 - Article
AN - SCOPUS:85136315887
SN - 1471-0684
VL - 22
SP - 606
EP - 622
JO - Theory and Practice of Logic Programming
JF - Theory and Practice of Logic Programming
IS - 4
ER -