Relating constraint answer set programming languages and algorithms

Recently a logic programming language AC was proposed by Mellarkod et al. [1] to integrate answer set programming and constraint logic programming. Soon after that, a clingcon language integrating answer set programming and finite domain constraints, as well as an ezcsp language integrating answer set programming and constraint logic programming were introduced. The development of these languages and systems constitutes the appearance of a new AI subarea called constraint answer set programming. All these languages have something in common. In particular, they aim at developing new efficient inference algorithms that combine traditional answer set programming procedures and other methods in constraint programming. Yet, the exact relation between the constraint answer set programming languages and the underlying systems is not well understood. In this paper we address this issue by formally stating the precise relation between several constraint answer set programming languages – AC, clingcon, ezcsp – as well as the underlying systems.