A Human Computation Framework for Boosting Combinatorial Solvers

Authors

  • Ronan Le Bras Cornell University
  • Yexiang Xue Cornell University
  • Richard Bernstein Cornell University
  • Carla Gomes Cornell University
  • Bart Selman Cornell University

DOI:

https://doi.org/10.1609/hcomp.v2i1.13155

Keywords:

Human Computation, Combinatorial Solvers, Materials Discovery

Abstract

We propose a general framework for boosting combinatorial solvers through human computation. Our framework combines insights from human workers with the power of combinatorial optimization. The combinatorial solver is also used to guide requests for the workers, and thereby obtain the most useful human feedback quickly. Our approach also incorporates a problem decomposition approach with a general strategy for discarding incorrect human input. We apply this framework in the domain of materials discovery, and demonstrate a speedup of over an order of magnitude.

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Published

2014-09-05

How to Cite

Le Bras, R., Xue, Y., Bernstein, R., Gomes, C., & Selman, B. (2014). A Human Computation Framework for Boosting Combinatorial Solvers. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 2(1), 121-132. https://doi.org/10.1609/hcomp.v2i1.13155