Distributed Inference in Dynamical Systems

Stanislav Funiak, Carlos Ernesto Guestrin, Mark Paskin, and Rahul Sukthankar
Advances in Neural Information Processing Systems 19, December, 2006.


Download
  • Adobe portable document format (pdf) (379KB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

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.

Keywords
Graphical Models, Sensor Networks

Notes
Sponsor: Intel Research Scholar Program, Alfred P. Sloan Fellowship
Grant ID: NSF-NeTS CNS-0625518, CNS-0428738 NSF ITR

Text Reference
Stanislav Funiak, Carlos Ernesto Guestrin, Mark Paskin, and Rahul Sukthankar, "Distributed Inference in Dynamical Systems," Advances in Neural Information Processing Systems 19, December, 2006.

BibTeX Reference
@inproceedings{Funiak_2006_5607,
   author = "Stanislav Funiak and Carlos Ernesto Guestrin and Mark Paskin and Rahul Sukthankar",
   title = "Distributed Inference in Dynamical Systems",
   booktitle = "Advances in Neural Information Processing Systems 19",
   publisher = "MIT Press",
   month = "December",
   year = "2006",
}