Complex Optimization in Answer Set Programming

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 and optimization capacities become readily available for ASP applications.