Sequential Prediction for Information Fusion and Control
Rebecca M. Willett, Maxim Raginsky · 2013
Abstract : The goal of this work was to develop sequential prediction methods for online information fusion and control, with methods designed to handle unknown, environmental dynamics, potentially stemming from an adversary who reacts to sensing actions, active sensing paradigms, and external feedback mechanisms. Online prediction and targeted collection of information is an emerging paradigm at the intersection of optimization, machine learning and control theory, which is concerned with real-time sequential planning of actions or decisions in the presence of model uncertainty, nonstationarity, and possibly adversarial disturbances. Several methods and underlying supporting theory which meet these objectives are described in detail in the following final report.