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Asynchronous Execution in Multiagent POMDPs: Reasoning Over Partially-Observable Events

João V. Messias, Matthijs T. J. Spaan, and Pedro U. Lima. Asynchronous Execution in Multiagent POMDPs: Reasoning Over Partially-Observable Events. In Multi-agent Sequential Decision Making under Uncertainty, 2013. Workshop at AAMAS13.

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Abstract

This paper proposes a novel modeling approach to problems of multiagent decision-making under partial observability, building on the framework of Multiagent Partial Observable Markov Decision Processes (MPOMDPs). Unfortunately, the size of MPOMDP models (and their solutions) grows exponentially in the number of agents, and agents are required to act in synchrony. In the present work, we show how these problems can be mitigated through an event-driven, asynchronous formulation of the MPOMDP dynamics. We introduce the necessary extensions to the dynamics and solution algorithms of standard MPOMDPs. In particular, we prove that the optimal value function in our Event-Driven Multiagent POMDP framework is piecewise linear and convex, allowing us to extend a standard point-based solver to the event-driven setting. Finally, we present simulation results, showing the computational savings of our modeling approach.

BibTeX Entry

@InProceedings{Messias13msdm,
  author =       {Jo{\~a}o V. Messias and Matthijs T. J. Spaan and
                  Pedro U. Lima},
  title =        {Asynchronous Execution in Multiagent {POMDPs}:
                  Reasoning Over Partially-Observable Events},
  booktitle =    {Multi-agent Sequential Decision Making under Uncertainty},
  year =         2013,
  note =         {Workshop at AAMAS13.}
}

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