@inproceedings{300b1aecc5064a7693f084e10e96fb6f,
title = "Distance-Penalized Active Learning via Markov Decision Processes",
abstract = "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.",
keywords = "Active learning, adaptive sampling, autonomous systems, mobile sensor, path planning",
author = "Dingyu Wang and John Lipor and Gautam Dasarathy",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Data Science Workshop, DSW 2019 ; Conference date: 02-06-2019 Through 05-06-2019",
year = "2019",
month = jun,
doi = "10.1109/DSW.2019.8755602",
language = "English (US)",
series = "2019 IEEE Data Science Workshop, DSW 2019 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "155--159",
booktitle = "2019 IEEE Data Science Workshop, DSW 2019 - Proceedings",
}