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.
NeuroComb: Improving SAT Solving with Graph Neural Networks Wenxi Wang, Yang Hu, Mohit Tiwari, Sarfraz Khurshid, Kenneth McMillan, Risto Miikkulainen arXiv:2110.14053, 2021. 2021

Surviving Solver Sensitivity: An ASP Practitioner's Guide Bryan Silverthorn, Yuliya Lierler and Marius Schneider In International Conference on Logic Programming (ICLP), 2012. 2012

A Probabilistic Architecture for Algorithm Portfolios Bryan Silverthorn PhD Thesis, Department of Computer Science, The University of Texas at Austin, 2012. 2012

Learning Polarity from Structure in SAT Bryan Silverthorn and Risto Miikkulainen In Theory and Applications of Satisfiability Testing (SAT), 2011. (extended abstract). 2011

Latent Class Models for Algorithm Portfolio Methods Bryan Silverthorn and Risto Miikkulainen In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010. 2010

Bryan Silverthorn Ph.D. Alumni bsilvert [at] cs utexas edu

The borg project includes a practical algorithm...