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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-baseddoi:10.1109/icpr.2006.317 dblp:conf/icpr/GormanTBH06 fatcat:v7xwuhrf4fhupgbl5xv3wgkrky