Data-driven prediction of EVAR with confidence in time-varying datasets
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Axelrod2016CDC_DataDrivenEVaR.pdf
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Author(s)
Axelrod, Allan
Carlone, Luca
Chowdhary, Girish
Karaman, Sertac
Date Issued
December 2016
Journal
2016 IEEE 55th Conference on Decision and Control (CDC)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Axelrod, Allan, et al. "Data-Driven Prediction of EVAR with Confidence in Time-Varying Datasets." 2016 IEEE 55th Conference on on Decision and Control (CDC), 12-14 December, 2016, Las Vegas, Nevada, IEEE, 2016, pp. 5833–38.
Version
Author's final manuscript
Abstract
The key challenge for learning-based autonomous systems operating in time-varying environments is to predict when the learned model may lose relevance. If the learned model loses relevance, then the autonomous system is at risk of making wrong decisions. The entropic value at risk (EVAR) is a computationally efficient and coherent risk measure that can be utilized to quantify this risk. In this paper, we present a Bayesian model and learning algorithms to predict the state-dependent EVAR of time-varying datasets. We discuss applications of EVAR to an exploration problem in which an autonomous agent has to choose a set of sensing locations in order to maximize the informativeness of the acquired data and learn a model of an underlying phenomenon of interest. We empirically demonstrate the efficacy of the presented model and learning algorithms on four real-world datasets.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CDC.2016.7799166
