Satometer:: how much have we searched?
Proceedings of the 39th annual Design Automation Conference
A Bayesian Approach to Tackling Hard Computational Problems
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Hierarchical hardness models for SAT
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Restart Strategy Selection Using Machine Learning Techniques
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Problem-Sensitive Restart Heuristics for the DPLL Procedure
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Predicting the size of IDA*'s search tree
Artificial Intelligence
Algorithm runtime prediction: Methods & evaluation
Artificial Intelligence
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We present an online method for estimating the cost of solving SAT problems. Modern SAT solvers present several challenges to estimate search cost including non-chronological backtracking, learning and restarts. Our method uses a linear model trained on data gathered at the start of search. We show the effectiveness of this method using random and structured problems. We demonstrate that predictions made in early restarts can be used to improve later predictions. We also show that we can use such cost estimations to select a solver from a portfolio.