Discrete Mathematics
Journal of the ACM (JACM)
Easy problems are sometimes hard
Artificial Intelligence
Extending and implementing the stable model semantics
Artificial Intelligence
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
Eighteenth national conference on Artificial intelligence
The DLV system for knowledge representation and reasoning
ACM Transactions on Computational Logic (TOCL)
Ten challenges in propositional reasoning and search
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Clasp: a conflict-driven answer set solver
LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
Testing and debugging techniques for answer set solver development
Theory and Practice of Logic Programming
Simple but hard mixed horn formulas
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
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We consider random logic programs with two-literal rules and study their properties. In particular, we obtain results on the probability that random "sparse" and "dense" programs with two-literal rules have answer sets. We study experimentally how hard it is to compute answer sets of such programs. For programs that are constraint-free and purely negative we show that the easy-hard-easy pattern emerges. We provide arguments to explain that behavior. We also show that the hardness of programs from the hard region grows quickly with the number of atoms. Our results point to the importance of purely negative constraint-free programs for the development of ASP solvers.