Reformulation of Global Constraints in Answer Set Programming

TL;DR

This paper presents a method to reformulate global constraints like all-different into answer set programs, enabling enhanced propagation and sharing of information between constraints, with promising experimental results.

Contribution

It introduces novel reformulations of global constraints into answer set programming that achieve various levels of consistency and facilitate better constraint interaction.

Findings

Reformulations achieve arc, bound, or range consistency.

Sharing variables improves propagation between constraints.

Experimental results demonstrate the effectiveness of the approach.

Abstract

We show that global constraints on finite domains like all-different can be reformulated into answer set programs on which we achieve arc, bound or range consistency. These reformulations offer a number of other advantages beyond providing the power of global propagators to answer set programming. For example, they provide other constraints with access to the state of the propagator by sharing variables. Such sharing can be used to improve propagation between constraints. Experiments with these encodings demonstrate their promise.