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We consider the problem of generating randomized control sequences for complex networked systems typically actuated by human agents. Our approach leverages a concept known as control improvisation, which is based on a combination of data-driven learning and controller synthesis from formal specifications. We learn from existing data a generative model (for instance, an explicit-duration hidden Markov model, or EDHMM) and then supervise this model in order to guarantee that the generateddoi:10.1109/iotdi.2015.33 dblp:conf/iotdi/AkkayaFVDLS16 fatcat:xkajtd2b5vhevielprk3cwnhpi