  Many logic programming based approaches can be used to describe and solve
combinatorial search problems. On the one hand there are definite programs and
constraint logic programs that compute a solution as an answer substitution to
a query containing the variables of the constraint satisfaction problem. On the
other hand there are approaches based on stable model semantics, abduction, and
first-order logic model generation that compute solutions as models of some
theory. This paper compares these different approaches from point of view of
knowledge representation (how declarative are the programs) and from point of
view of performance (how good are they at solving typical problems).
