This generalization has applications to answer set programming.
Major logic programming language families include Prolog, Answer set programming ( ASP ) and Datalog.
But the use of stable models in answer set programming provides a different perspective on such programs.
Typically, however, a translation is given from these languages to answer set programming rather than first-order logic.
The autoepistemic interpretation was developed further by Gelfond and Lifschitz [ 1988 ] and is the basis of answer set programming.
The autoepistemic interpretation of NAF can be combined with classical negation, as in extended logic programming and answer set programming.
By comparison, answer set programming is also based on predicates ( more precisely, on atomic sentences created from atomic formula ).
As an alternative to the completion semantics, negation as failure can also be interpreted epistemically, as in the stable model semantics of answer set programming.
From this point of view, logic programs with exactly one stable model are rather special in answer set programming, like polynomials with exactly one root in algebra.
VGDL can be used to describe a game specifically for procedural generation of levels, using Answer Set Programming ( ASP ) and an Evolutionary Algorithm ( EA ).