Privacy Sensitive Construction of Junction Tree Agent Organization for Multiagent Graphical Models
Abstract
Junction trees (JTs) are not only effective structures for single-agent probabilistic graphical models (PGMs), but also effective agent organizations in multiagent graphical models, such as multiply sectioned Bayesian networks. A natural decomposition of agent environment may not allow construction of a JT organization. Hence, re-decomposition of the environment is necessary. However, re-decomposition incurs loss of agent privacy that ultimately translates to loss of intellectual property of agent suppliers. We propose a novel algorithm DAER (Distributed Agent Environment Re-decomposition) that re-decomposes the environment to enable a JT organization and incurs significantly less privacy loss than existing JT organization construction methods.
Cite
Text
Xiang and Alshememry. "Privacy Sensitive Construction of Junction Tree Agent Organization for Multiagent Graphical Models." Proceedings of the Ninth International Conference on Probabilistic Graphical Models, 2018.Markdown
[Xiang and Alshememry. "Privacy Sensitive Construction of Junction Tree Agent Organization for Multiagent Graphical Models." Proceedings of the Ninth International Conference on Probabilistic Graphical Models, 2018.](https://mlanthology.org/pgm/2018/xiang2018pgm-privacy/)BibTeX
@inproceedings{xiang2018pgm-privacy,
title = {{Privacy Sensitive Construction of Junction Tree Agent Organization for Multiagent Graphical Models}},
author = {Xiang, Yang and Alshememry, Abdulrahman},
booktitle = {Proceedings of the Ninth International Conference on Probabilistic Graphical Models},
year = {2018},
pages = {523-534},
volume = {72},
url = {https://mlanthology.org/pgm/2018/xiang2018pgm-privacy/}
}