The problem of propositional satisfiability (SAT) is the classic NP-complete problem. It asks whether a Boolean expression is satisfiable: whether an assignment of Boolean values to its variables exists that makes the expression true. Algorithms for determining satisfiability underpin methods in numerous application domains, including planning, constraint satisfaction, and software and hardware verification. Our work on satisfiability focuses on developing and testing portfolio methods.
Bryan Silverthorn Ph.D. Alumni bsilvert [at] cs utexas edu
A Probabilistic Architecture for Algorithm Portfolios 2012
Bryan Silverthorn, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Surviving Solver Sensitivity: An ASP Practitioner's Guide 2012
Bryan Silverthorn, Yuliya Lierler and Marius Schneider, International Conference on Logic Programming (ICLP) (2012).
Learning Polarity from Structure in SAT 2011
Bryan Silverthorn and Risto Miikkulainen, In Theory and Applications of Satisfiability Testing (SAT) 2011. (extended abstract).
Latent Class Models for Algorithm Portfolio Methods 2010
Bryan Silverthorn and Risto Miikkulainen, In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence 2010.

The borg project includes a practical algorithm...