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Multiagent Planning under Uncertainty with Stochastic Communication Delays

Matthijs T. J. Spaan, Frans A. Oliehoek, and Nikos Vlassis. Multiagent Planning under Uncertainty with Stochastic Communication Delays. In Proceedings of the Eighteenth International Conference on Automated Planning and Scheduling, pp. 338–345, September 2008.

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Abstract

We consider the problem of cooperative multiagent planning under uncertainty, formalized as a decentralized partially observable Markov decision process (Dec-POMDP). By communicating their local observations before they take actions, the agents synchronize their knowledge of the environment, and the planning problem reduces to a centralized POMDP. As such, relying on communication significantly reduces the complexity of planning. In the real world however, such communication might fail temporarily. We present a step towards more realistic communication models for Dec-POMDPs by proposing a model that: (1) allows that communication might be delayed by one or more stages, and (2) explicitly reasons about the probability of successful communication in the future. For our model, we discuss how to efficiently compute an (approximate) value function and corresponding policies, and we demonstrate our theoretical results with encouraging experiments.

BibTeX Entry

@InProceedings{Spaan08ICAPS,
  author =       {Matthijs T. J. Spaan and Frans A. Oliehoek and 
                  Nikos Vlassis},
  title =        {Multiagent  Planning under Uncertainty with Stochastic
                  Communication Delays},
  booktitle =    ICAPS08,
  month =        sep,
  year =         2008,
  pages =        {338--345},
  url =         {https://www.aaai.org/Papers/ICAPS/2008/ICAPS08-042.pdf},
  abstract = 	 {
  We consider the problem of cooperative multiagent planning under
  uncertainty, formalized as a decentralized partially observable
  Markov decision process (Dec-POMDP).  By communicating their local
  observations before they take actions, the agents synchronize their
  knowledge of the environment, and the planning problem reduces to a
  centralized POMDP. As such, relying on communication significantly
  reduces the complexity of planning. In the real world however, such
  communication might fail temporarily. We present a step towards more
  realistic communication models for Dec-POMDPs by proposing a model
  that: (1) allows that communication might be delayed by one or more
  stages, and (2) explicitly reasons about the probability of
  successful communication in the future.  For our model, we discuss
  how to efficiently compute an (approximate) value function and
  corresponding policies, and we demonstrate our theoretical results
  with encouraging experiments.
  }
}

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