Exploiting locality of interaction in factored Dec-POMDPs

  • Authors:

  • Frans A. Oliehoek;Matthijs T. J. Spaan;Shimon Whiteson;Nikos Vlassis

  • Affiliations:

  • University of Amsterdam, Amsterdam, The Netherlands;Institute for Systems Robotics, Lisbon, Portugal;University of Amsterdam, Amsterdam, The Netherlands;University of Crete, Chania, Greece

  • Venue:
  • Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
  • Year:
  • 2008

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Abstract

Decentralized partially observable Markov decision processes (Dec-POMDPs) constitute an expressive framework for multiagent planning under uncertainty, but solving them is provably intractable. We demonstrate how their scalability can be improved by exploiting locality of interaction between agents in a factored representation. Factored Dec-POMDP representations have been proposed before, but only for Dec-POMDPs whose transition and observation models are fully independent. Such strong assumptions simplify the planning problem, but result in models with limited applicability. By contrast, we consider general factored Dec-POMDPs for which we analyze the model dependencies over space (locality of interaction) and time (horizon of the problem). We also present a formulation of decomposable value functions. Together, our results allow us to exploit the problem structure as well as heuristics in a single framework that is based on collaborative graphical Bayesian games (CGBGs). A preliminary experiment shows a speedup of two orders of magnitude.