Complex Optimization in Answer Set Programming
Martin Gebser, Roland Kaminski, Torsten Schaub

TL;DR
This paper introduces a meta-programming approach to enable complex optimization and preference handling in Answer Set Programming, making advanced features more accessible and easier to implement.
Contribution
It presents a general meta-programming technique that leverages existing ASP systems to support complex preferences and optimization criteria.
Findings
Enables inclusion-based minimization in ASP
Supports Pareto efficiency in answer set optimization
Simplifies implementation of complex preferences
Abstract
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…
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