  Constraint answer set programming or CASP, for short, is a hybrid approach in
automated reasoning putting together the advances of distinct research areas
such as answer set programming, constraint processing, and satisfiability
modulo theories. Constraint answer set programming demonstrates promising
results, including the development of a multitude of solvers: acsolver,
clingcon, ezcsp, idp, inca, dingo, mingo, aspmt, clingo[l,dl], and ezsmt. It
opens new horizons for declarative programming applications such as solving
complex train scheduling problems. Systems designed to find solutions to
constraint answer set programs can be grouped according to their construction
into, what we call, integrational or translational approaches. The focus of
this paper is an overview of the key ingredients of the design of constraint
answer set solvers drawing distinctions and parallels between integrational and
translational approaches. The paper also provides a glimpse at the kind of
programs its users develop by utilizing a CASP encoding of Travelling Salesman
problem for illustration. In addition, we place the CASP technology on the map
among its automated reasoning peers as well as discuss future possibilities for
the development of CASP.
