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

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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.

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