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Learning to select branching rules in the DPLL procedure for satisfiability

Lagoudakis Michael, Littman, M.

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/85854029-9F0F-4C83-9CBA-F91FC42A21D0-
Αναγνωριστικόhttps://www.cs.ubc.ca/~hutter/EARG.shtml/earg/papers07/lagoudakis01learning.pdf-
Γλώσσαen-
Μέγεθος16 pagesen
ΤίτλοςLearning to select branching rules in the DPLL procedure for satisfiabilityen
ΔημιουργόςLagoudakis Michaelen
ΔημιουργόςΛαγουδακης Μιχαηλel
Δημιουργός Littman, M.en
ΠερίληψηThe 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
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2015-11-14-
Ημερομηνία Δημοσίευσης2001-
Θεματική ΚατηγορίαDPLL procedureen
Βιβλιογραφική ΑναφοράM. 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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