Robotic intelligence with behavior selection network for Bayesian network ensemble
Keum-Sung Hwang, Han-Saem Park, Sung‐Bae Cho · 2009
Scene understanding is an important and difficult problem in intelligent robotics and computer vision. Since visual information is uncertain due to several reasons, we need a novel method that has robustness to the uncertainty. Bayesian probabilistic approach is robust to manage the uncertainty and powerful to model high-level contexts. Moreover, Bayesian network can be adapted to environment efficiently by learning. In this paper, we propose a Bayesian network ensemble technique based on behavior selection network. The method includes how to handle uncertainty based on probabilistic approach, and how to combine multiple Bayesian networks. An experiment with a mobile robot simulation presents how the proposed ensemble method works and can be used effectively.