Two-Literal Logic Programs and Satisfiability Representation of Stable Models: A Comparison
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
ASSAT: computing answer sets of a logic program by SAT solvers
Artificial Intelligence - Special issue on nonmonotonic reasoning
Propositional Satisfiability and Constraint Programming: A comparative survey
ACM Computing Surveys (CSUR)
Combining answer set programming with description logics for the Semantic Web
Artificial Intelligence
Integrating answer set programming and constraint logic programming
Annals of Mathematics and Artificial Intelligence
Logic programs with abstract constraint atoms
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
An experimental comparison of constraint logic programming and answer set programming
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Answer sets for logic programs with arbitrary abstract constraint atoms
Journal of Artificial Intelligence Research
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The strength of answer set programming (ASP) lies in solving computationally challenging problems declaratively, and hopefully efficiently. A similar goal is shared by two other approaches, SAT and Constraint Programming (CP). As future applications of ASP hinge on its underlying solving techniques, in this note, I will briefly comment on the related techniques, and argue for the need of ASP systems to integrate with state-of-the-art techniques for constraint solving, and in general to serve as the core reasoning engine to glue other logics and reasoning mechanisms together.