  We propose a data-driven framework to enable the modeling and optimization of
human-machine interaction processes, e.g., systems aimed at assisting humans in
decision-making or learning, work-load allocation, and interactive advertising.
This is a challenging problem for several reasons. First, humans' behavior is
hard to model or infer, as it may reflect biases, long term memory, and
sensitivity to sequencing, i.e., transience and exponential complexity in the
length of the interaction. Second, due to the interactive nature of such
processes, the machine policy used to engage with a human may bias possible
data-driven inferences. Finally, in choosing machine policies that optimize
interaction rewards, one must, on the one hand, avoid being overly sensitive to
error/variability in the estimated human model, and on the other, being overly
deterministic/predictable which may result in poor human 'engagement' in the
interaction. To meet these challenges, we propose a robust approach, based on
the maximum entropy principle, which iteratively estimates human behavior and
optimizes the machine policy--Alternating Entropy-Reward Ascent (AREA)
algorithm. We characterize AREA, in terms of its space and time complexity and
convergence. We also provide an initial validation based on synthetic data
generated by an established noisy nonlinear model for human decision-making.
