A message passing approach to multiagent gaussian inference for dynamic processes

  • Authors:
  • Stefano Ermon;Carla Gomes;Bart Selman

  • Affiliations:
  • Cornell University, Ithaca, New York;Cornell University, Ithaca, New York;Cornell University, Ithaca, New York

  • Venue:
  • The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
  • Year:
  • 2011

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Abstract

In [1], we introduced a novel distributed inference algorithm for the multiagent Gaussian inference problem, based on the framework of graphical models and message passing algorithms. We compare it to current state of the art techniques and we demonstrate that it is the most efficient one in terms of communication resources used. Moreover, we show experimentally that it outperforms the other methods in terms of estimation error on a general class of problems, even in presence of data loss.