Hidden Markov Models derived from Behavior Trees
Robotics
2019-07-24 v1 Artificial Intelligence
Abstract
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.
Keywords
Cite
@article{arxiv.1907.10029,
title = {Hidden Markov Models derived from Behavior Trees},
author = {Blake Hannaford},
journal= {arXiv preprint arXiv:1907.10029},
year = {2019}
}
Comments
Submitted to IEEE Transactions on Robotics and Automation, 23-Jul-2019