Optimizing Interventions via Offline Policy Evaluation: Studies in Citizen Science


  • Avi Segal Ben-Gurion University of the Negev
  • Kobi Gal Ben-Gurion University of the Negev
  • Ece Kamar Microsoft Research, Redmond WA
  • Eric Horvitz Microsoft Research, Redmond WA
  • Grant Miller University of Oxford




Citizen Science, Off-line Policy Evaluation


Volunteers who help with online crowdsourcing such as citizen science tasks typically make only a few contributions before exiting. We propose a computational approach for increasing users' engagement in such settings that is based on optimizing policies for displaying motivational messages to users. The approach, which we refer to as Trajectory Corrected Intervention (TCI), reasons about the tradeoff between the long-term influence of engagement messages on participants' contributions and the potential risk of disrupting their current work. We combine model-based reinforcement learning with off-line policy evaluation to generate intervention policies, without relying on a fixed representation of the domain. TCI works iteratively to learn the best representation from a set of random intervention trials and to generate candidate intervention policies. It is able to refine selected policies off-line by exploiting the fact that users can only be interrupted once per session.We implemented TCI in the wild with Galaxy Zoo, one of the largest citizen science platforms on the web. We found that TCI was able to outperform the state-of-the-art intervention policy for this domain, and significantly increased the contributions of thousands of users. This work demonstrates the benefit of combining traditional AI planning with off-line policy methods to generate intelligent intervention strategies.




How to Cite

Segal, A., Gal, K., Kamar, E., Horvitz, E., & Miller, G. (2018). Optimizing Interventions via Offline Policy Evaluation: Studies in Citizen Science. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11489



AAAI Technical Track: Human-AI Collaboration