Nieuwenhuis, Oliveras, and Tinelli (2006) showed how to describe enhancements of the Davis-Putnam-Logemann-Loveland algorithm using transition systems, instead of pseudocode. We design a similar framework for several algorithms that generate answer sets for logic programs: Smodels, Smodels_cc, Asp-sat with Learning, Cmodels, and a newly designed and implemented algorithm Sup. This approach to describing answer set solvers makes it easier to prove their correctness, to compare them, and to design new systems.

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Theory and Practice of Logic Programming (2011).

@article{lierler:aaspl, title={Abstract Answer Set Solvers with Backjumping and Learning}, author={Yuliya Lierler}, journal={Theory and Practice of Logic Programming}, url="http://www.cs.utexas.edu/users/ai-lab?lierler:aaspl", year={2011} }