We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated according to (noisy) observations from experts, whose behaviors are modeled using discrete types. We propose recommendation techniques, inference methods, and query selection strategies to assist a user charged with choosing a configuration that satisfies some (partially known) concept. Our model is able to simultaneously learn the concept definition and the types of the experts. We evaluate our model with simulations, showing that our Bayesian strategies are effective even in large concept spaces with many uninformative experts. © 2011 Springer-Verlag.
CITATION STYLE
Viappiani, P., Zilles, S., Hamilton, H. J., & Boutilier, C. (2011). Learning complex concepts using crowdsourcing: A Bayesian approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6992 LNAI, pp. 277–291). https://doi.org/10.1007/978-3-642-24873-3_21
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