Quantifying over Optimum Answer Sets

TL;DR

This paper extends Answer Set Programming with Quantifiers (ASP(Q)) by incorporating weak constraints, enabling elegant modeling of optimization problems within the polynomial hierarchy, and analyzes its computational complexity.

Contribution

It introduces an extension of ASP(Q) with weak constraints for local and global optimization, and studies its computational properties.

Findings

Enhanced modeling capabilities for optimization problems.

Complexity results for the extended ASP(Q) formalism.

Illustrative application scenarios demonstrating the approach.

Abstract

Answer Set Programming with Quantifiers (ASP(Q)) has been introduced to provide a natural extension of ASP modeling to problems in the polynomial hierarchy (PH). However, ASP(Q) lacks a method for encoding in an elegant and compact way problems requiring a polynomial number of calls to an oracle in $\Sigma_n^p$ (that is, problems in $\Delta_{n+1}^p$). Such problems include, in particular, optimization problems. In this paper we propose an extension of ASP(Q), in which component programs may contain weak constraints. Weak constraints can be used both for expressing local optimization within quantified component programs and for modeling global optimization criteria. We showcase the modeling capabilities of the new formalism through various application scenarios. Further, we study its computational properties obtaining complexity results and unveiling non-obvious characteristics of ASP(Q) programs with weak constraints.