Crowdsourcing Backdoor Identification for Combinatorial Optimization
Abstract
We will show how human computation insights can be key to identifying so-called backdoor variables in combinatorial optimization problems. Backdoor variables can be used to obtain dramatic speed- ups in combinatorial search. Our approach leverages the complementary strength of human input, based on a visual identification of problem structure, crowdsourcing, and the power of combinatorial solvers to exploit complex constraints. We describe our work in the context of the domain of materials discovery. The motivation for considering the materials discovery domain comes from the fact that new materials can provide solutions for key challenges in sustainability, e.g., in energy, new catalysts for more efficient fuel cell technology.
Cite
Text
LeBras et al. "Crowdsourcing Backdoor Identification for Combinatorial Optimization." International Joint Conference on Artificial Intelligence, 2013.Markdown
[LeBras et al. "Crowdsourcing Backdoor Identification for Combinatorial Optimization." International Joint Conference on Artificial Intelligence, 2013.](https://mlanthology.org/ijcai/2013/lebras2013ijcai-crowdsourcing/)BibTeX
@inproceedings{lebras2013ijcai-crowdsourcing,
title = {{Crowdsourcing Backdoor Identification for Combinatorial Optimization}},
author = {LeBras, Ronan and Bernstein, Richard and Gomes, Carla P. and Selman, Bart and van Dover, R. Bruce},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2013},
pages = {2840-2847},
url = {https://mlanthology.org/ijcai/2013/lebras2013ijcai-crowdsourcing/}
}