Complex Optimization in Answer Set Programming

Details Complex Optimization in Answer Set Programming Complex Optimization in Answer Set Programming

2011-07-28

arxiv.org/abs/1107.5742v1

Theory and Practice of Logic Programming, 11(4-5), 821-839, 2011

Computer Science

Logic in Computer Science

18 pages, 5 figures

Scientific paper

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.

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