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Artificial Intelligence Review
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ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Tight bounds on local search to approximate the maximum satisfiability problems
COCOON'11 Proceedings of the 17th annual international conference on Computing and combinatorics
A CSP solver focusing on FAC variables
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Evaluating las vegas algorithms: pitfalls and remedies
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Reasoning, metareasoning, and mathematical truth: studies of theorem proving under limited resources
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Penalty logic and its link with Dempster-Shafer theory
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
On the impact of small-world on local search
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
UBCSAT: an implementation and experimentation environment for SLS algorithms for SAT and MAX-SAT
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
Relative generic computational forensic techniques
IH'04 Proceedings of the 6th international conference on Information Hiding
Alloy+HotCore: a fast approximation to unsat core
ABZ'10 Proceedings of the Second international conference on Abstract State Machines, Alloy, B and Z
Symmetry breaking and local search spaces
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Solving over-constrained problems with SAT technology
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A symbolic search based approach for quantified boolean formulas
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Diversification and determinism in local search for satisfiability
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Random walk with continuously smoothed variable weights
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Constraint metrics for local search
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Improved exact solvers for weighted Max-SAT
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Mining chemical compound structure data using inductive logic programming
AM'03 Proceedings of the Second international conference on Active Mining
Introduction to inconsistency tolerance
Inconsistency Tolerance
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IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
GRASP with path-relinking for the weighted maximum satisfiability problem
WEA'05 Proceedings of the 4th international conference on Experimental and Efficient Algorithms
SAT solving with reference points
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Dynamic scoring functions with variable expressions: new SLS methods for solving SAT
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An evolutionary local search algorithm for the satisfiability problem
TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
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Approximate boolean reasoning: foundations and applications in data mining
Transactions on Rough Sets V
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LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
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Applied Soft Computing
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ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
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Cognitive Systems Research
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Annals of Mathematics and Artificial Intelligence
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Constraints
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Evolutionary Computation
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AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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Multiagent and Grid Systems - Principles and Practice of Multi-Agent Systems
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We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional approaches such as the Davis-Putnam procedure or resolution. We also show that GSAT can solve structured satisfiability problems quickly. In particular, we solve encodings of graph coloring problems, N-queens, and Boolean induction. General application strategies and limitations of the approach are also discussed. GSAT is best viewed as a model-finding procedure. Its good performance suggests that it may be advantageous to reformulate reasoning tasks that have traditionally been viewed as theorem-proving problems as model-finding tasks.