Principles of automated theorem proving
Principles of automated theorem proving
Machine Learning
Automated Theory Formation in Pure Mathematics
Automated Theory Formation in Pure Mathematics
An Open-Ended Finite Domain Constraint Solver
PLILP '97 Proceedings of the9th International Symposium on Programming Languages: Implementations, Logics, and Programs: Including a Special Trach on Declarative Programming Languages in Education
Constraint Generation via Automated Theory Formation
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
CGRASS: a system for transforming constraint satisfaction problems
ERCIM'02/CologNet'02 Proceedings of the 2002 Joint ERCIM/CologNet international conference on Constraint solving and constraint logic programming
A SAT-based version space algorithm for acquiring constraint satisfaction problems
ECML'05 Proceedings of the 16th European conference on Machine Learning
A Global Workspace Framework for Combining Reasoning Systems
Proceedings of the 9th AISC international conference, the 15th Calculemas symposium, and the 7th international MKM conference on Intelligent Computer Mathematics
Tailoring solver-independent constraint models: a case study with ESSENCE' and MINION
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
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
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A well-known difficulty with solving Constraint Satisfaction Problems (CSPs) is that, while one formulation of a CSP may enable a solver to solve it quickly, a different formulation may take prohibitively long to solve. We demonstrate a system for automatically reformulating CSP solver models by combining the capabilities of machine learning and automated theorem proving with CSP systems. Our system is given a basic CSP formulation and outputs a set of reformulations, each of which includes additional constraints. The additional constraints are generated through a machine learning process and are proven to follow from the basic formulation by a theorem prover. Experimenting with benchmark problem classes from finite algebras, we show how the time invested in reformulation is often recovered many times over when searching for solutions to more difficult problems from the problem class.