Experimental results on the application of satisfiability algorithms to scheduling problems
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Symbolic model checking using SAT procedures instead of BDDs
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Evaluating general purpose automated theorem proving systems
Artificial Intelligence
Machine Learning
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
Learning dynamic algorithm portfolios
Annals of Mathematics and Artificial Intelligence
Learning parallel portfolios of algorithms
Annals of Mathematics and Artificial Intelligence
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
SATzilla: portfolio-based algorithm selection for SAT
Journal of Artificial Intelligence Research
Unifying SAT-based and graph-based planning
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
A portfolio approach to algorithm select
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Algorithm selection and scheduling
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
A bayesian approach to tackling hard computational problems
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Using CBR to select solution strategies in constraint programming
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Combinational test generation using satisfiability
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Snappy: a simple algorithm portfolio
SAT'13 Proceedings of the 16th international conference on Theory and Applications of Satisfiability Testing
Algorithm portfolios based on cost-sensitive hierarchical clustering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Algorithm runtime prediction: Methods & evaluation
Artificial Intelligence
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Portfolio-based methods exploit the complementary strengths of a set of algorithms and--as evidenced in recent competitions--represent the state of the art for solving many NP-hard problems, including SAT. In this work, we argue that a state-of-the-art method for constructing portfolio-based algorithm selectors, $\texttt{SATzilla}$, also gives rise to an automated method for quantifying the importance of each of a set of available solvers. We entered a substantially improved version of $\texttt{SATzilla}$ to the inaugural "analysis track" of the 2011 SAT competition, and draw two main conclusions from the results that we obtained. First, automatically-constructed portfolios of sequential, non-portfolio competition entries perform substantially better than the winners of all three sequential categories. Second, and more importantly, a detailed analysis of these portfolios yields valuable insights into the nature of successful solver designs in the different categories. For example, we show that the solvers contributing most to $\texttt{SATzilla}$ were often not the overall best-performing solvers, but instead solvers that exploit novel solution strategies to solve instances that would remain unsolved without them.