Privacy Sensitive Construction of Junction Tree Agent Organization for Multiagent Graphical Models

Yang Xiang, Abdulrahman Alshememry
; Proceedings of the Ninth International Conference on Probabilistic Graphical Models, PMLR 72:523-534, 2018.

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 this Paper


BibTeX
@InProceedings{pmlr-v72-xiang18a, 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}, pages = {523--534}, year = {2018}, editor = {Václav Kratochvíl and Milan Studený}, volume = {72}, series = {Proceedings of Machine Learning Research}, address = {Prague, Czech Republic}, month = {11--14 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v72/xiang18a/xiang18a.pdf}, url = {http://proceedings.mlr.press/v72/xiang18a.html}, 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.} }
Endnote
%0 Conference Paper %T Privacy Sensitive Construction of Junction Tree Agent Organization for Multiagent Graphical Models %A Yang Xiang %A Abdulrahman Alshememry %B Proceedings of the Ninth International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2018 %E Václav Kratochvíl %E Milan Studený %F pmlr-v72-xiang18a %I PMLR %J Proceedings of Machine Learning Research %P 523--534 %U http://proceedings.mlr.press %V 72 %W PMLR %X 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.
APA
Xiang, Y. & Alshememry, A.. (2018). Privacy Sensitive Construction of Junction Tree Agent Organization for Multiagent Graphical Models. Proceedings of the Ninth International Conference on Probabilistic Graphical Models, in PMLR 72:523-534

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