  Answer set programming is a leading declarative constraint programming
paradigm with wide use for complex knowledge-intensive applications. Modern
answer set programming languages support many equivalent ways to model
constraints and specifications in a program. However, so far answer set
programming has failed to develop systematic methodologies for building
representations that would uniformly lend well to automated processing. This
suggests that encoding selection, in the same way as algorithm selection and
portfolio solving, may be a viable direction for improving performance of
answer-set solving. The necessary precondition is automating the process of
generating possible alternative encodings. Here we present an automated
rewriting system, the Automated Aggregator or AAgg, that given a non-ground
logic program, produces a family of equivalent programs with complementary
performance when run under modern answer set programming solvers. We
demonstrate this behavior through experimental analysis and propose the
system's use in automated answer set programming solver selection tools.
