  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 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.
