Local Consistency and SAT-Solvers
In this paper we show that the power of using k-consistency techniques in a constraint problem is precisely captured by using a particular inference rule, which we call positive-hyper-resolution, on the direct Boolean encoding of the CSP instance. We also show that current clause-learning SAT-solver...
| Main Authors: | , |
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| Format: | Journal article |
| Language: | English |
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2010
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| _version_ | 1797054073086672896 |
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| author | Petke, J Jeavons, P |
| author_facet | Petke, J Jeavons, P |
| author_sort | Petke, J |
| collection | OXFORD |
| description | In this paper we show that the power of using k-consistency techniques in a constraint problem is precisely captured by using a particular inference rule, which we call positive-hyper-resolution, on the direct Boolean encoding of the CSP instance. We also show that current clause-learning SAT-solvers will deduce any positive-hyper-resolvent of a fixed size from a given set of clauses in polynomial expected time. We combine these two results to show that, without being explicitly designed to do so, current clause-learning SAT-solvers efficiently simulate k-consistency techniques, for all values of k. We then give some experimental results to show that this feature allows clause-learning SAT-solvers to efficiently solve certain families of CSP instances which are challenging for conventional CP solvers. © 2010 Springer-Verlag. |
| first_indexed | 2024-03-06T18:52:26Z |
| format | Journal article |
| id | oxford-uuid:10b3c2da-425b-452e-8089-223a67a88c34 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T18:52:26Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | oxford-uuid:10b3c2da-425b-452e-8089-223a67a88c342022-03-26T09:57:54ZLocal Consistency and SAT-SolversJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:10b3c2da-425b-452e-8089-223a67a88c34EnglishSymplectic Elements at Oxford2010Petke, JJeavons, PIn this paper we show that the power of using k-consistency techniques in a constraint problem is precisely captured by using a particular inference rule, which we call positive-hyper-resolution, on the direct Boolean encoding of the CSP instance. We also show that current clause-learning SAT-solvers will deduce any positive-hyper-resolvent of a fixed size from a given set of clauses in polynomial expected time. We combine these two results to show that, without being explicitly designed to do so, current clause-learning SAT-solvers efficiently simulate k-consistency techniques, for all values of k. We then give some experimental results to show that this feature allows clause-learning SAT-solvers to efficiently solve certain families of CSP instances which are challenging for conventional CP solvers. © 2010 Springer-Verlag. |
| spellingShingle | Petke, J Jeavons, P Local Consistency and SAT-Solvers |
| title | Local Consistency and SAT-Solvers |
| title_full | Local Consistency and SAT-Solvers |
| title_fullStr | Local Consistency and SAT-Solvers |
| title_full_unstemmed | Local Consistency and SAT-Solvers |
| title_short | Local Consistency and SAT-Solvers |
| title_sort | local consistency and sat solvers |
| work_keys_str_mv | AT petkej localconsistencyandsatsolvers AT jeavonsp localconsistencyandsatsolvers |