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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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Related topics

Related topics

6 relations
AlgorithmBackjumpingBacktrackingConstraint satisfaction problem

Broader (1)

Constraint programming

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2016
2016
Improving SAT Solvers by Exploiting Empirical Characteristics of CDCL
The Boolean Satisfiability Problem (SAT) is a canonical decision problem originally shown to be NP-complete in Cook’s seminal… 
2014
2014
Non-Restarting SAT Solvers with Simple Preprocessing Can Efficiently Simulate Resolution
Propositional satisfiability (SAT) solvers based on conflict directed clause learning (CDCL) implicitly produce resolution… 
2014
2014
On conflict driven clause learning – a comparison between theory and practice
The boolean satisfiability (SAT) problem is the canonical NPcomplete problem and every other NP-complete problem can be reduced… 
2010
2010
Extending Clause Learning of SAT Solvers with Boolean Gröbner Bases
We extend clause learning as performed by most modern SAT Solvers by integrating the computation of Boolean Grobner bases into… 
2010
2010
Heuristic Planning with SAT: Beyond Uninformed Depth-First Search
  • J. Rintanen
  • Australasian Conference on Artificial…
  • 2010
  • Corpus ID: 8180114
Planning-specific heuristics for SAT have recently been shown to produce planners that match best earlier ones that use other… 
2009
2009
Learning in Local Search
In this paper a learning based local search approach for propositional satisfiability is presented. It is based on an original… 
2007
2007
Using More Reasoning to Improve #SAT Solving
Many real-world problems, including inference in Bayes Nets, can be reduced to #SAT, the problem of counting the number of models… 
2007
2007
A Case for Simple SAT Solvers
As SAT becomes more popular due to its ability to handle large real-world problems, progress in efficiency appears to have slowed… 
2007
2007
IMPACT OF RESTRICTED BRANCHING ON CLAUSE LEARNING SAT SOLVING
Propositional satisfiability (SAT) solving procedures (or SAT solvers) are used as efficient back-end search engines in solving… 
2004
2004
Underspecification of intersective modifier attachment: Some arguments from German
In this paper, I shall discuss the semantic attachment of intersective modifiers in German coherent constructions. I shall show… 
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