  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 generated sequences satisfy some desirable
specifications given in Probabilistic Computation Tree Logic (PCTL). We present
an implementation of our approach and apply it to the problem of mimicking the
use of lighting appliances in a residential unit, with potential applications
to home security and resource management. We present experimental results
showing that our approach produces realistic control sequences, similar to
recorded data based on human actuation, while satisfying suitable formal
requirements.
