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Iterative Planning for Deterministic QDec-POMDPs

14 pagesPublished: September 17, 2018


QDec-POMDPs are a qualitative alternative to stochastic Dec-POMDPs for goal-oriented plan- ning in cooperative partially observable multi-agent environments. Although QDec-POMDPs share the same worst case complexity as Dec-POMDPs, previous research has shown an ability to scale up to larger domains while producing high quality plan trees. A key difficulty in distributed execution is the need to construct a joint plan tree branching on the combinations of observations of all agents. In this work, we suggest an iterative algorithm, IMAP, that plans for one agent at a time, taking into considerations collaboration constraints about action execution of previous agents, and generating new constraints for the next agents. We explain how these constraints are generated and handled, and a backtracking mechanism for changing constraints that cannot be met. We provide experimental results on multi-agent planning domains, showing our methods to scale to much larger problems with several collaborating agents and huge state spaces.

Keyphrases: contingent planning, distributed planning, multi-agent planning, Planning under uncertainty, POMDPs

In: Daniel Lee, Alexander Steen and Toby Walsh (editors). GCAI-2018. 4th Global Conference on Artificial Intelligence, vol 55, pages 15--28

BibTeX entry
  author    = {Sagi Bazinin and Guy Shani},
  title     = {Iterative Planning for Deterministic QDec-POMDPs},
  booktitle = {GCAI-2018. 4th Global Conference on Artificial Intelligence},
  editor    = {Daniel Lee and Alexander Steen and Toby Walsh},
  series    = {EPiC Series in Computing},
  volume    = {55},
  pages     = {15--28},
  year      = {2018},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2398-7340},
  url       = {},
  doi       = {10.29007/4t8s}}
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