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Satisfiability
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.
People
Bryan Silverthorn
Ph.D. Alumni
bsilvert [at] cs utexas edu
Publications
NeuroComb: Improving SAT Solving with Graph Neural Networks
2024
Wenxi Wang, Yang Hu, Mohit Tiwari, Sarfraz Khurshid, Kenneth McMillan, Risto Miikkulainen, In
Proceedings of the International Conference on Learning Representations
, 2024. (also arXiv:2110.14053).
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.
Projects
Borg: A General-Purpose Algorithm Portfolio System
2009 - 2013
Software/Data
Borg
The borg project
includes a practical algorithm...
2011
Demos
Model-Based Visualization of Solver Performance Data
Bryan Silverthorn
2011
Labs
Neural Networks