Modern interactive computer games provide the ability to objectively record complex human behavior, offering a variety of interesting challenges to the pattern-recognition community. Such recordings often represent a multiplexing of long-term strategy, mid-term tactics and short-term reactions, in addition to the more low-level details of the player?s movements. In this paper, we describe our work in the field of imitation learning; more specifically, we present a mature, Bayesian-based approach to the extraction of both the strategic behavior and movement patterns of a human player, and their use in realizing a cloned artificial agent. We then describe a set of experiments demonstrating the effectiveness of our model.