Crowdsourcing backdoor identification for combinatorial optimization

Authors:
Ronan Le Bras;Richard Bernstein;Carla P. Gomes;Bart Selman;R. Bruce Van Dover
Affiliations:
Computer Science Dept., Cornell University, Ithaca, NY;Computer Science Dept., Cornell University, Ithaca, NY;Computer Science Dept., Cornell University, Ithaca, NY;Computer Science Dept., Cornell University, Ithaca, NY;Materials Science and Engr. Dept., Cornell University, Ithaca, NY
Venue:
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Year:
2013

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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 speedups 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.