  Symmetry in combinatorial problems is an extensively studied topic. We
continue this research in the context of model expansion problems, with the aim
of automating the workflow of detecting and breaking symmetry. We focus on
local domain symmetry, which is induced by permutations of domain elements, and
which can be detected on a first-order level. As such, our work is a
continuation of the symmetry exploitation techniques of model generation
systems, while it differs from more recent symmetry breaking techniques in
answer set programming which detect symmetry on ground programs. Our main
contributions are sufficient conditions for symmetry of model expansion
problems, the identification of local domain interchangeability, which can
often be broken completely, and efficient symmetry detection algorithms for
both local domain interchangeability as well as local domain symmetry in
general. Our approach is implemented in the model expansion system IDP, and we
present experimental results showcasing the strong and weak points of our
approach compared to SBASS , a symmetry breaking technique for answer set
programming.
