Uncertainty in Artificial Intelligence
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Distribution over Beliefs for Memory Bounded Dec-POMDP Planning
Gabriel Corona, Francois Charpillet
Abstract:
We propose a new point-based method for approximate planning in Dec-POMDP which outperforms the state-of-the-art approaches in terms of solution quality. It uses a heuristic estimation of the prior probability of beliefs to choose a bounded number of policy trees: this choice is formulated as a combinatorial optimisation problem minimising the error induced by pruning.
Keywords:
Pages: 135-142
PS Link:
PDF Link: /papers/10/p135-corona.pdf
BibTex:
@INPROCEEDINGS{Corona10,
AUTHOR = "Gabriel Corona and Francois Charpillet",
TITLE = "Distribution over Beliefs for Memory Bounded Dec-POMDP Planning",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "135--142"
}


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