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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… 
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Papers overview

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2017
2017
Motivated by the question of how to e � ciently do model finding or theorem proving for multi-valued logics, we study the relative… 
2015
2015
We present an algorithm, CDCL-AMS, for solving Modular Systems consisting of a set of modules where, for each module, we have a… 
2015
2015
I am currently roughly 9 months into my PhD studies, and the final topic of my PhD thesis is not yet fully settled. My current… 
2014
2014
The boolean satisfiability (SAT) problem is the canonical NPcomplete problem and every other NP-complete problem can be reduced… 
2014
2014
Conflict-directed clause learning (CDCL) is the basis of SAT solvers with impressive performance on many problems. CDCL with… 
2012
2012
Satisfiability solvers targeting industrial instances are currently almost always based on conflict-driven clause learning (CDCL… 
2010
2010
In this paper a new learning scheme for SAT is proposed. The originality of our approach arises from its ability to achieve… 
2009
2009
Several learning systems based on Inverse Entailment (IE) have been proposed, some that compute single clause hypotheses… 
2009
2009
Search-based techniques in propositional satisfiability (SAT) solving have been enormously successful, leading to what is…