Lecture: Lifting Symmetry Breaking Constraints with Inductive LogicProgramming
Master data
Title: | Lifting Symmetry Breaking Constraints with Inductive LogicProgramming |
Description: | 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 since the computed SBCs are propositional and, therefore, can neither be meaningfully interpreted nor transferred to other instances. As a result, a time-consuming recomputation of SBCs must be done before every invocation of a solver. To 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. Experiments demonstrate the ability of our framework to learn general constraints from instancespecific 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. |
Keywords: | Answer Set Programming, Inductive Logic Programming, Symmetry BreakingConstraints |
Type: | Registered lecture |
Homepage: | http://lr2020.iit.demokritos.gr/accepted/index.html |
Event: | 1st International Joint Conference on Learning & Reasoning IJCLR 2021 (Athens) |
Date: | 27.10.2021 |
lecture status: | stattgefunden (online) |
Participants
Alice Tarzariol (internal) |
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Martin Gebser (internal) |
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Konstantin Schekotihin (internal) |
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Assignment
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Fakultät für Technische Wissenschaften
Institut für Artificial Intelligence und Cybersecurity
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AT - A-9020 Klagenfurt |
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