Data-driven Prediction of EVAR with Confidence in Time-varying Datasets

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.

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Exploitation by Informed Exploration between Isolated Operatives for Information-theoretic Data Harvesting

We consider the problem of ferrying data between nodes of a sparsely distributed sensing network of Unattended Ground Sensors (UGS) with endurance-constrained Unmanned Aerial Systems (UAS). The sensing domain wherein the sparsely distributed UGS network is deployed is assumed to be highly nonstationary (time-varying) and noisy. This makes the dataferrying problem very complicated as the expected value-of-information at a sensing location can rapidly change. To address this issue, we present a new data ferrying algorithm termed Exploitation by Informed Exploration between Isolated Operatives (EIEIO), and show that with several reasonable assumptions and a model on the predicted accumulation of value-of-information, the problem can be simplified to a mathematical linear program. To solve the linear program, the UAS learns to anticipate regions in the sensing domain that have the highest degree of change. The degree of change, is learned using a novel implementation of a Cox Process called the Cox-Gaussian Process (CGP). Our approach does not require a priori knowledge of the sensing domain model to arrive at an optimal UAS allocation strategy.

Given knowledge of the informatic content of all sampling locations, a closed path may be formed so the data-ferrying agent visits the most informative subset of data source locations.

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