Distribution over Beliefs for Memory Bounded Dec-POMDP Planning
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.
Cite
Text
Corona and Charpillet. "Distribution over Beliefs for Memory Bounded Dec-POMDP Planning." Conference on Uncertainty in Artificial Intelligence, 2010.Markdown
[Corona and Charpillet. "Distribution over Beliefs for Memory Bounded Dec-POMDP Planning." Conference on Uncertainty in Artificial Intelligence, 2010.](https://mlanthology.org/uai/2010/corona2010uai-distribution/)BibTeX
@inproceedings{corona2010uai-distribution,
title = {{Distribution over Beliefs for Memory Bounded Dec-POMDP Planning}},
author = {Corona, Gabriel and Charpillet, François},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2010},
pages = {135-142},
url = {https://mlanthology.org/uai/2010/corona2010uai-distribution/}
}