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Complex optimization in answer set programming

Published online by Cambridge University Press:  06 July 2011

MARTIN GEBSER ,
ROLAND KAMINSKI  and
TORSTEN SCHAUB
MARTIN GEBSER
Affiliation:
Institut für Informatik, Universität Potsdam, Germany
ROLAND KAMINSKI
Affiliation:
Institut für Informatik, Universität Potsdam, Germany
TORSTEN SCHAUB
Affiliation:
Institut für Informatik, Universität Potsdam, Germany
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Abstract

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Preference handling and optimization are indispensable means for addressing nontrivial 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-prpogramming, 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.

Keywords

Answer Set ProgrammingPreference HandlingComplex optimizationMeta-Programming

Information

Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 11 , Special Issue 4-5: 27th International Conference on Logic Programming , July 2011 , pp. 821 - 839
Copyright
Copyright © Cambridge University Press 2011

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