Towards a Language for Non-Expert Specification of POMDPs for Crowdsourcing

Authors

  • Christopher Lin University of Washington
  • - Mausam University of Washington
  • Daniel Weld University of Washington

DOI:

https://doi.org/10.1609/hcomp.v1i1.13117

Keywords:

POMDP, planning, reinforcement learning, utility elicitation

Abstract

Crowdsourcing requesters are trapped between a rock and a hard place. Typically they specify their crowdsourcing workflows procedurally, but current languages commit them to overly strict and static policies that waste human effort. While optimizing workflows with more sophisticated tools like POMDPs can significantly reduce labor costs, such advanced AI techniques are hard to use and understand. We report on our progress in developing Clowder, a system that provides the user with an adaptive programming language that looks and feels like Lisp, yet abstracts over POMDPs so that non-experts can write POMDPs without knowing anything about them. Such a system frees requesters from needing to resort to suboptimal techniques that use approximate heuristics or hire a planning expert to formally define and solve their problems.

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Published

2013-11-03

How to Cite

Lin, C., Mausam, .-., & Weld, D. (2013). Towards a Language for Non-Expert Specification of POMDPs for Crowdsourcing. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 1(1), 46-47. https://doi.org/10.1609/hcomp.v1i1.13117

Issue

Vol. 1 (2013): First AAAI Conference on Human Computation and Crowdsourcing

Section

Works in Progress