  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.
