Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability
Abstract
This paper provides the first formalization of self-interested planning in multiagent settings using expectation-maximization (EM). Our formalization in the context of infinite-horizon and finitely-nested interactive POMDPs (I-POMDP) is distinct from EM formulations for POMDPs and cooperative multiagent planning frameworks. We exploit the graphical model structure specific to I-POMDPs, and present a new approach based on block-coordinate descent for further speed up. Forward filtering-backward sampling -- a combination of exact filtering with sampling -- is explored to exploit problem structure.
Cite
Text
Qu and Doshi. "Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability." Neural Information Processing Systems, 2015.Markdown
[Qu and Doshi. "Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability." Neural Information Processing Systems, 2015.](https://mlanthology.org/neurips/2015/qu2015neurips-individual/)BibTeX
@inproceedings{qu2015neurips-individual,
title = {{Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability}},
author = {Qu, Xia and Doshi, Prashant},
booktitle = {Neural Information Processing Systems},
year = {2015},
pages = {478-486},
url = {https://mlanthology.org/neurips/2015/qu2015neurips-individual/}
}