Abstract Answer Set Solvers with Backjumping and Learning (2011)
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.
Theory and Practice of Logic Programming (2011).

Yuliya Lierler Ph.D. Alumni ylierler [at] unomaha edu