Abstract Answer Set Solvers with Learning

Details Abstract Answer Set Solvers with Learning Abstract Answer Set Solvers with Learning

2010-01-06

arxiv.org/abs/1001.0820v1

Computer Science

Artificial Intelligence

Long version of the paper that will appear in special issue of Theory and Practice of Logic Programming

Scientific paper

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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