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… (More)
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Topic mentions per year

1986-2018
010203019862018

Papers overview

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Highly Cited
2012
Highly Cited
2012
Decision procedures for Boolean satisfiability (SAT), especially modern conflict-driven clause learning (CDCL) solvers, act… (More)
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2012
2012
We prove that the graph tautology principles of Alekhnovich, Johannsen, Pitassi and Urquhart have polynomial size pool resolution… (More)
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2006
2006
We present a method to convert the construction of binary decision diagrams (BDDs) into extended resolution proofs. Besides in… (More)
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Highly Cited
2005
Highly Cited
2005
An important extension of satisfiability testing is model-counting, a task that corresponds to problems such as probabilistic… (More)
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Highly Cited
2004
Highly Cited
2004
Efficient implementations of DPLL with the addition of clause learning are the fastest complete Boolean satisfiability solvers… (More)
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Highly Cited
2002
Highly Cited
2002
The backtracking based Davis Putnam (DPLL) procedure remains the dominant method for deciding the satisfiability of a CNF formula… (More)
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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… (More)
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Highly Cited
1993
Highly Cited
1993
Many AI synthesis problems such as planning, scheduling or design may be encoded in a constraint satisfaction problems (CSP). A… (More)
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1986
1986
The popular use of backtracking as a control strategy for theorem proving in PROLOG and in Truth-MaintenanceSystems (TMS) led to… (More)
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