Driving CDCL Search

November 16, 2016 Β· Declared Dead Β· πŸ› arXiv.org

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Authors Carmine Dodaro, Philip Gasteiger, Nicola Leone, Benjamin Musitsch, Francesco Ricca, Konstantin Schekotihin arXiv ID 1611.05190 Category cs.AI: Artificial Intelligence Citations 1 Venue arXiv.org Last Checked 4 months ago
Abstract
The CDCL algorithm is the leading solution adopted by state-of-the-art solvers for SAT, SMT, ASP, and others. Experiments show that the performance of CDCL solvers can be significantly boosted by embedding domain-specific heuristics, especially on large real-world problems. However, a proper integration of such criteria in off-the-shelf CDCL implementations is not obvious. In this paper, we distill the key ingredients that drive the search of CDCL solvers, and propose a general framework for designing and implementing new heuristics. We implemented our strategy in an ASP solver, and we experimented on two industrial domains. On hard problem instances, state-of-the-art implementations fail to find any solution in acceptable time, whereas our implementation is very successful and finds all solutions.
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