Hidden Markov Models derived from Behavior Trees

Understand

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.