Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Angluin, D.: Queries and concept learning. Machine Learning 2, 319–342 (1988)
Boutilier, C., Regan, K., Viappiani, P.: Online feature elicitation in interactive optimization. In: Proceedings of the Twenty-sixth International Conference on Machine Learning (ICML 2009), Montreal, pp. 73–80 (2009)
Boutilier, C., Regan, K., Viappiani, P.: Simultaneous elicitation of preference features and utility. In: Proceedings of the Twenty-fourth AAAI Conference on Artificial Intelligence (AAAI 2010), Atlanta, pp. 1160–1167 (2010)
Chen, S., Zhang, J., Chen, G., Zhang, C.: What if the irresponsible teachers are dominating? In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2010), Atlanta, pp. 419–424 (2010)
Dai, P., Mausam, Weld, D.S.: Decision-theoretic control of crowd-sourced workflows. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2010), Atlanta, pp. 1168–1174 (2010)
Haussler, D.: Learning conjunctive concepts in structural domains. Machine Learning 4, 7–40 (1989)
Heckerman, D., Horvitz, E., Middleton, B.: An approximate nonmyopic computation for value of information. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(3), 292–298 (1993)
Howard, R.: Information value theory. IEEE Transactions on Systems Science and Cybernetics 2(1), 22–26 (1966)
Kearns, M.J., Li, M.: Learning in the presence of malicious errors. SIAM Journal on Computing 22, 807–837 (1993)
Mitchell, T.M.: Version spaces: A candidate elimination approach to rule learning. In: Proceedings of the Fifth International Joint Conference on Artificial Intelligence (IJCAI 1977), Cambridge, pp. 305–310 (1977)
Shahaf, D., Horvitz, E.: Generalized task markets for human and machine computation. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2010), Atlanta, pp. 986–993 (2010)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Viappiani, P., Boutilier, C.: Optimal Bayesian recommendation sets and myopically optimal choice query sets. In: Advances in Neural Information Processing Systems (NIPS), Vancouver, vol. 23, pp. 2352–2360 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Viappiani, P., Zilles, S., Hamilton, H.J., Boutilier, C. (2011). Learning Complex Concepts Using Crowdsourcing: A Bayesian Approach. In: Brafman, R.I., Roberts, F.S., Tsoukiàs, A. (eds) Algorithmic Decision Theory. ADT 2011. Lecture Notes in Computer Science(), vol 6992. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24873-3_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-24873-3_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24872-6
Online ISBN: 978-3-642-24873-3
eBook Packages: Computer ScienceComputer Science (R0)