Asynchronous Dynamic Bayesian Networks
  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.
