  The task of keyhole (unobtrusive) plan recognition is central to adaptive
game AI. "Tech trees" or "build trees" are the core of real-time strategy (RTS)
game strategic (long term) planning. This paper presents a generic and simple
Bayesian model for RTS build tree prediction from noisy observations, which
parameters are learned from replays (game logs). This unsupervised machine
learning approach involves minimal work for the game developers as it leverage
players' data (com- mon in RTS). We applied it to StarCraft1 and showed that it
yields high quality and robust predictions, that can feed an adaptive AI.
