Crowdsourcing Backdoor Identification for Combinatorial Optimization
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 2840-2846.
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.
@inproceedings{ijcai2013p418,
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 = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {2840--2846},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/418},
}
TY - CPAPER TI - Crowdsourcing Backdoor Identification for Combinatorial Optimization AU - LeBras, Ronan AU - Bernstein, Richard AU - Gomes, Carla P. AU - Selman, Bart AU - van Dover, R. Bruce T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 2840 EP - 2846 UR - https://ijcai.org/proceedings/2013/418 ER -Copied
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