Dual viewpoint heuristics for binary constraint satisfaction problems
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Constructing Small Sample Spaces Satisfying Given Constants
SIAM Journal on Discrete Mathematics
Generating random solutions for constraint satisfaction problems
Eighteenth national conference on Artificial intelligence
Finding diverse and similar solutions in constraint programming
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Constraint patterns and search procedures for CP-based random test generation
HVC'07 Proceedings of the 3rd international Haifa verification conference on Hardware and software: verification and testing
Cost-driven interactive CSP with constraint relaxation
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Constraint-based local search for the automatic generation of architectural tests
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Functional test generation with distribution constraints
HVC'09 Proceedings of the 5th international Haifa verification conference on Hardware and software: verification and testing
A new algorithm for sampling CSP solutions uniformly at random
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Generating diverse solutions in SAT
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
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We examine the behavior of dynamic value-ordering heuristics in a CSP under the requirement to generate a large number of diverse solutions as fast as possible. In particular, we analyze the trade-off between the solution search performance and the diversity of the generated solutions, and propose a general probabilistic approach to control and improve this trade-off. Several old/new learning-reuse heuristics are described, extending the survivors-first value-ordering heuristics family. The proposed approach is illustrated on a real-world set of examples from the Automatic Test Generation problem domain, as well as on several sets of random binary CSPs.