URI | http://purl.tuc.gr/dl/dias/85854029-9F0F-4C83-9CBA-F91FC42A21D0 | - |
Identifier | https://www.cs.ubc.ca/~hutter/EARG.shtml/earg/papers07/lagoudakis01learning.pdf | - |
Language | en | - |
Extent | 16 pages | en |
Title | Learning to select branching rules in the DPLL procedure for satisfiability | en |
Creator | Lagoudakis Michael | en |
Creator | Λαγουδακης Μιχαηλ | el |
Creator | Littman, M. | en |
Content Summary | The 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 Item | Πλήρης Δημοσίευση σε Συνέδριο | el |
Type of Item | Conference Full Paper | en |
License | http://creativecommons.org/licenses/by/4.0/ | en |
Date of Item | 2015-11-14 | - |
Date of Publication | 2001 | - |
Subject | DPLL procedure | en |
Bibliographic Citation | M. 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.pdf | en |