  Behavior trees are rapidly attracting interest in robotics and human
task-related motion tracking. However no algorithms currently exist to track or
identify parameters of BTs under noisy observations. We report a new
relationship between BTs, augmented with statistical information, and Hidden
Markov Models. Exploiting this relationship will allow application of many
algorithms for HMMs (and dynamic Bayesian networks) to data acquired from
BT-based systems.
