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Abstract
A wellknown 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.
Original language  English 

Title of host publication  ECAI 2006, PROCEEDINGS 
Editors  G Brewka, S Coraeschi, A Perini, P Traverso 
Place of Publication  AMSTERDAM 
Publisher  I O S PRESS 
Pages  7377 
Number of pages  5 
ISBN (Print)  9781586036423 
Publication status  Published  2006 
Event  17th European Conference on Artificial Intelligence  Riva del Garda, Italy Duration: 29 Aug 2006 → 1 Sept 2006 
Conference
Conference  17th European Conference on Artificial Intelligence 

Country/Territory  Italy 
City  Riva del Garda 
Period  29/08/06 → 1/09/06 
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Dive into the research topics of 'Automatic Generation of Implied Constraints'. Together they form a unique fingerprint.Projects
 1 Finished

EP/C523229/1: Multidisciplinary Critical Mass in Computational Algebra and Applications
Linton, S. A., Gent, I. P., Leonhardt, U., Mackenzie, A., Miguel, I. J., Quick, M. & Ruskuc, N.
1/09/05 → 31/08/10
Project: Standard