  We consider the problem of synthetically generating data that can closely
resemble human decisions made in the context of an interactive human-AI system
like a computer game. We propose a novel algorithm that can generate synthetic,
human-like, decision making data while starting from a very small set of
decision making data collected from humans. Our proposed algorithm integrates
the concept of reward shaping with an imitation learning algorithm to generate
the synthetic data. We have validated our synthetic data generation technique
by using the synthetically generated data as a surrogate for human interaction
data to solve three sequential decision making tasks of increasing complexity
within a small computer game-like setup. Different empirical and statistical
analyses of our results show that the synthetically generated data can
substitute the human data and perform the game-playing tasks almost
indistinguishably, with very low divergence, from a human performing the same
tasks.
