Distance-Penalized Active Learning via Markov Decision Processes

Dingyu Wang, John Lipor, Gautam Dasarathy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


We consider the problem of active learning in the context of spatial sampling, where the measurements are obtained by a mobile sampling unit. The goal is to localize the change point of a one-dimensional threshold classifier while minimizing the total sampling time, a function of both the cost of sampling and the distance traveled. In this paper, we present a general framework for active learning by modeling the search problem as a Markov decision process. Using this framework, we present time-optimal algorithms for the spatial sampling problem when there is a uniform prior on the change point, a known non-uniform prior on the change point, and a need to return to the origin for intermittent battery recharging. We demonstrate through simulations that our proposed algorithms significantly outperform existing methods while maintaining a low computational cost.

Original languageEnglish (US)
Title of host publication2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781728107080
StatePublished - Jun 2019
Event2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States
Duration: Jun 2 2019Jun 5 2019

Publication series

Name2019 IEEE Data Science Workshop, DSW 2019 - Proceedings


Conference2019 IEEE Data Science Workshop, DSW 2019
Country/TerritoryUnited States


  • Active learning
  • adaptive sampling
  • autonomous systems
  • mobile sensor
  • path planning

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality


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