J. Franke

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Acknowledgements So very many people have touched my life over the course of this odyssey. For those that may escape mention here|a dissertation in itself|I have made the last to be rst, and I perhaps owe you a drink. Resounding thanks and a c horus of gratitude to my advisor, David Leake, for all of his support, guidance, and encouragement. David has(More)
Applications that have access to user intent and task context can support better, faster decision-making on the part of the user. In this paper, we present AUTOS, an approach to the implementation of individual and team intent inference. AUTOS uses observable contextual clues to infer current operator task state and predict future task state. Guided by the(More)
We describe a nonlinear regression problem, where the regression functions have an additive structure and the dependent variable is a one-dimensional time series. Multivariate time series with unknown time delay operators are used as independent variables. By fitting a feedforward neural network with block structure to the data, we estimated the additive(More)
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