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Learning to select branching rules in the DPLL procedure for satisfiability

Lagoudakis Michael, Littman, M.

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URIhttp://purl.tuc.gr/dl/dias/85854029-9F0F-4C83-9CBA-F91FC42A21D0-
Identifierhttps://www.cs.ubc.ca/~hutter/EARG.shtml/earg/papers07/lagoudakis01learning.pdf-
Languageen-
Extent16 pagesen
TitleLearning to select branching rules in the DPLL procedure for satisfiabilityen
CreatorLagoudakis Michaelen
CreatorΛαγουδακης Μιχαηλel
Creator Littman, M.en
Content SummaryThe DPLL procedure is the most popular complete satisfiability (SAT) solver. While its worst case complexity is exponential, the actual running time is greatly affected by the ordering of branch variables during the search. Several branching rules have been proposed, but none is the best in all cases. This work investigates the use of automated methods for choosing the most appropriate branching rule at each node in the search tree. We consider a reinforcementlearning approach where a value function, which predicts the performance of each branching rule in each case, is learned through trial runs on a typical problem set of the target class of SAT problems. Our results indicate that, provided sufficient training on a given class, the resulting strategy performs as well as (and, in some cases, better than) the best branching rule for that class. en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-14-
Date of Publication2001-
SubjectDPLL procedureen
Bibliographic CitationM. G. Lagoudakis and M. L. Littman. (2001, June). Learning to select branching rules in the DPLL procedure for satisfiability. [Online]. Available: https://www.cs.ubc.ca/~hutter/EARG.shtml/earg/papers07/lagoudakis01learning.pdfen

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