Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Propositional Logic: Deduction and Algorithms
Propositional Logic: Deduction and Algorithms
A framework for learning constraints: Preliminary report
PRICAI '96 Selected Papers from the Workshop on Reasoning with Incomplete and Changing Information and on Inducing Complex Representations: Learning and Reasoning with Complex Representations
Suggestion Strategies for Constraint-Based Matchmaker Agents
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
Version spaces and the consistency problem
Artificial Intelligence
Polynomial-time learning with version spaces
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Version spaces without boundary sets
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Exploiting automatically inferred constraint-models for building identification in satellite imagery
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Automatic Generation of Implied Constraints
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Acquiring constraint networks using a SAT-based version space algorithm
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Query-driven constraint acquisition
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Generalized constraint acquisition
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
A constraint seeker: finding and ranking global constraints from examples
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Soft constraints of difference and equality
Journal of Artificial Intelligence Research
A model seeker: extracting global constraint models from positive examples
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Constraint acquisition via partial queries
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Constraint programming is rapidly becoming the technology of choice for modelling and solving complex combinatorial problems. However, users of this technology need significant expertise in order to model their problems appropriately. The lack of availability of such expertise is a significant bottleneck to the broader uptake of constraint technology in the real world. We present a new SAT-based version space algorithm for acquiring constraint satisfaction problems from examples of solutions and non-solutions of a target problem. An important advantage is the ease with which domain-specific knowledge can be exploited using the new algorithm. Finally, we empirically demonstrate the algorithm and the effect of exploiting domain-specific knowledge on improving the quality of the acquired constraint network.