neural networks research group
areas
people
projects
demos
publications
software/data
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.
Publications
None
NeuroComb: Improving SAT Solving with Graph Neural Networks
Wenxi Wang, Yang Hu, Mohit Tiwari, Sarfraz Khurshid, Kenneth McMillan, Risto Miikkulainen
In
Proceedings of the International Conference on Learning Representations
, 2024. (also arX...
2024
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
People
None
Bryan Silverthorn
Ph.D. Alumni
bsilvert [at] cs utexas edu
Projects
None
Borg: A General-Purpose Algorithm Portfolio System
2009 - 2013
Demos
None
Model-Based Visualization of Solver Performance Data
Bryan Silverthorn
2011
Software/Data
None
Borg
The borg project
includes a practical algorithm...
2011