Asynchronous Dynamic Bayesian Networks
This addresses the challenge of probabilistic reasoning in decentralized systems where nodes operate independently and the state evolves continuously, offering an incremental improvement over static models.
The paper tackles the problem of monitoring distributed asynchronous systems like sensor networks and robot teams by introducing a dynamic model approach based on belief propagation, which outperforms the factored frontier algorithm in experiments.
Systems such as sensor networks and teams of autonomous robots consist of multiple autonomous entities that interact with each other in a distributed, asynchronous manner. These entities need to keep track of the state of the system as it evolves. Asynchronous systems lead to special challenges for monitoring, as nodes must update their beliefs independently of each other and no central coordination is possible. Furthermore, the state of the system continues to change as beliefs are being updated. Previous approaches to developing distributed asynchronous probabilistic reasoning systems have used static models. We present an approach using dynamic models, that take into account the way the system changes state over time. Our approach, which is based on belief propagation, is fully distributed and asynchronous, and allows the world to keep on changing as messages are being sent around. Experimental results show that our approach compares favorably to the factored frontier algorithm.