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Distributed Inference in Dynamical Systems

 

In Advances in Neural Information Processing Systems 19

Stanislav Funiak, Carlos Guestrin, Mark Paskin, and Rahul Sukthankar

pages 433–440, Cambridge, MA

December, 2006

Abstract


download pdf


@inproceedings{funiak-nips06,
  title = {Distributed Inference in Dynamical Systems},
  author = {Funiak, Stanislav and Guestrin, Carlos and Paskin, Mark
     and Sukthankar, Rahul},
  booktitle = {Advances in Neural Information Processing Systems 19},
  venue = {Advances in Neural Information Processing Systems},
  editor = {B. Scholkopf and J. Platt and T. Hoffman},
  publisher = {MIT Press},
  address = {Cambridge, MA},
  pages = {433--440},
  year = {2006},
  month = {December},
  url = {http://www.cs.cmu.edu/~claytronics/papers/funiak-nips06.pdf},
  keywords = {Probabilistic Inference, Sensing, Distributed
     Algorithms, Graphical Models},
  abstract = {We present a robust distributed algorithm for
     approximate probabilistic inference in dynamical systems, such as
     sensor networks and teams of mobile robots. Using assumed density
     filtering, the network nodes maintain a tractable representation
     of the belief state in a distributed fashion. At each time step,
     the nodes coordinate to condition this distribution on the
     observations made throughout the network, and to advance this
     estimate to the next time step. In addition, we identify a
     significant challenge for probabilistic inference in dynamical
     systems: message losses or network partitions can cause nodes to
     have inconsistent beliefs about the current state of the system.
     We address this problem by developing distributed algorithms that
     guarantee that nodes will reach an informative consistent
     distribution when communication is re-established. We present a
     suite of experimental results on real-world sensor data for two
     real sensor network deployments: one with 25 cameras and another
     with 54 temperature sensors.},
}

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