AI-MIX: Using Automated Planning to Steer Human Workers Towards Better Crowdsourced Plans

Authors

  • Lydia Manikonda Arizona State University
  • Tathagata Chakraborti Arizona State University
  • Sushovan De Arizona State University
  • Kartik Talamadupula Arizona State University
  • Subbarao Kambhampati Arizona State University

Abstract

Human computation applications that involve planning and scheduling are gaining popularity, and the existing literature on such systems shows that any automated oversight on human contributors improves the effectiveness of the crowd. In this paper, we present our ongoing work on the AI-MIX system, which is a first step towards using an automated planning and scheduling system in a crowdsourced planning application. In order to address the mismatch between the capabilities of the crowd and the automated planner, we identify two major challenges -- interpretation, and steering. We also present preliminary empirical results over the tour planning domain, and show how using an automated planner can help improve the quality of plans.

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Published

2014-09-05

How to Cite

Manikonda, L., Chakraborti, T., De, S., Talamadupula, K., & Kambhampati, S. (2014). AI-MIX: Using Automated Planning to Steer Human Workers Towards Better Crowdsourced Plans. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 2(1). Retrieved from https://ojs.aaai.org/index.php/HCOMP/article/view/13192