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Constraint learning

Known as: Clause learning, Relevance-bounded learning 
In constraint satisfaction backtracking algorithms, constraint learning is a technique for improving efficiency. It works by recording new… Expand
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2018
2018
Modern CDCL (conflict-driven clause learning) SAT solvers are used for many practical applications. One of the key ingredients of… Expand
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2016
2016
We developed a formal framework for CDCL conflict-driven clause learning in Isabelle/HOL. Through a chain of refinements, an… Expand
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Highly Cited
2015
Highly Cited
2015
The VSIDS (variable state independent decaying sum) decision heuristic invented in the context of the CDCL (conflict-driven… Expand
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Highly Cited
2013
Highly Cited
2013
This paper introduces WASP, an ASP solver handling disjunctive logic programs under the stable model semantics. WASP implements… Expand
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2012
2012
In recent years, Parallel SAT solvers have leveraged with the so called Parallel Portfolio architecture. In this setting, a… Expand
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2010
2010
Search based solvers for Quantified Boolean Formulas (QBF) have adapted the SAT solver techniques of unit propagation and clause… Expand
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2008
2008
ManySat is a DPLL-engine which includes all the classical features like twowatched-literal, unit propagation, activity-based… Expand
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2008
2008
In this paper, we present a new way to preprocess Boolean formulae in Conjunctive Normal Form (CNF). In contrast to most of the… Expand
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Highly Cited
1996
Highly Cited
1996
Learning during backtrack search is a space-intensive process that records information (such as additional constraints) in order… Expand
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Highly Cited
1986
Highly Cited
1986
The popular use of backtracking as a control strategy for theorem proving in PROLOG and in Truth-Maintenance-Systems (TMS) led to… Expand
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