ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
A machine program for theorem-proving
Communications of the ACM
SAT-Encodings, Search Space Structure, and Local Search Performance
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
To Encode or Not to Encode - Linear Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Generating Satisfiable Problem Instances
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Solving the Round Robin Problem Using Propositional Logic
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Capturing Structure with Satisfiability
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Control Abstractions for Local Search
Constraints
Automatic SAT-compilation of planning problems
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Heuristics based on unit propagation for satisfiability problems
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Solving non-Boolean satisfiability problems with stochastic local search
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Balance and filtering in structured satisfiable problems
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Integer optimization by local search: a domain-independent approach
Integer optimization by local search: a domain-independent approach
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Problem structure in the presence of perturbations
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Solving linear pseudo-Boolean constraint problems with local search
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Deploy, adjust and readjust: supporting dynamic reconfiguration of policy enforcement
Middleware'11 Proceedings of the 12th ACM/IFIP/USENIX international conference on Middleware
Parallel search for maximum satisfiability
AI Communications - 18th RCRA International Workshop on “Experimental evaluation of algorithms for solving problems with combinatorial explosion”
Perfect hashing and CNF encodings of cardinality constraints
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
Deploy, adjust and readjust: supporting dynamic reconfiguration of policy enforcement
Proceedings of the 12th International Middleware Conference
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Much excitement has been generated by the success of stochastic local search procedures at finding solutions to large, very hard satisfiability problems. Many of the problems on which these procedures have been effective are non-Boolean in that they are most naturally formulated in terms of variables with domain sizes greater than two. Approaches to solving non-Boolean satisfiability problems fall into two categories. In the direct approach, the problem is tackled by an algorithm for non-Boolean problems. In the transformation approach, the non-Boolean problem is reformulated as an equivalent Boolean problem and then a Boolean solver is used.This paper compares four methods for solving non-Boolean problems: one direct and three transformational. The comparison first examines the search spaces confronted by the four methods, and then tests their ability to solve random formulas, the round-robin sports scheduling problem, and the quasigroup completion problem. The experiments show that the relative performance of the methods depends on the domain size of the problem and that the direct method scales better as domain size increases.Along the route to performing these comparisons we make three other contributions. First, we generalize Walksat, a highly successful stochastic local search procedure for Boolean satisfiability problems, to work on problems with domains of any finite size. Second, we introduce a new method for transforming non-Boolean problems to Boolean problems and improve on an existing transformation. Third, we identify sufficient conditions for omitting at-least-one and at-most-one clauses from a transformed formula. Fourth, for use in our experiments we propose a model for generating random formulas that vary in domain size but are similar in other respects.