UTCS Artificial Intelligence
Algorithm portfolio methods operate in problem domains for which there are multiple algorithms with complementary strengths. The portfolio method applies patterns learned from experience to better allocate computational resources among the algorithms, attempting to apply each algorithm primarily to those problem instances to which it is best suited. Applications of the methods developed in this work include SAT and answer set programming.
bsilvert [at] cs utexas edu
A Probabilistic Architecture for Algorithm Portfolios
Bryan Silverthorn, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Surviving Solver Sensitivity: An ASP Practitioner's Guide
Bryan Silverthorn, Yuliya Lierler and Marius Schneider,
International Conference on Logic Programming (ICLP)
Latent Class Models for Algorithm Portfolio Methods
Bryan Silverthorn and Risto Miikkulainen, In
Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence
Borg: A General-Purpose Algorithm Portfolio System
2009 - 2013
The borg project
includes a practical algorithm...
Model-Based Visualization of Solver Performance Data