  Preference handling and optimization are indispensable means for addressing
non-trivial applications in Answer Set Programming (ASP). However, their
implementation becomes difficult whenever they bring about a significant
increase in computational complexity. As a consequence, existing ASP systems do
not offer complex optimization capacities, supporting, for instance,
inclusion-based minimization or Pareto efficiency. Rather, such complex
criteria are typically addressed by resorting to dedicated modeling techniques,
like saturation. Unlike the ease of common ASP modeling, however, these
techniques are rather involved and hardly usable by ASP laymen. We address this
problem by developing a general implementation technique by means of
meta-programming, thus reusing existing ASP systems to capture various forms of
qualitative preferences among answer sets. In this way, complex preferences and
optimization capacities become readily available for ASP applications.
