A number of frameworks for decentralized probabilistic reasoning, constraint reasoning, and decision theoretic reasoning assume a junction tree agent organization (JT-org). A natural decomposition of agent environment may not admit a JT-org. Hence, JT-org construction involves three related tasks: (1) Recognize whether a JT-org exists for a given environment decomposition. (2) When JT-orgs exist, construct one. (3) When no JT-org exists, revise the environment decomposition so that one exists and then construct it. Task 3 requires re-decomposition of the environment. However, re-decomposition incurs loss of JT-org linked privacy, including agent privacy, topology privacy, privacy on private variables, and privacy on shared variables. We propose a novel algorithm suite Distributed Agent Environment Re-decomposition (DAER) that accomplishes all three tasks distributively. For Tasks 1 and 2, DAER incurs no loss of JT-org linked privacy. For Task 3, it incurs significantly less privacy loss than existing JT-org construction methods. Performance of DAER is formally analyzed and empirically evaluated.

中文翻译：

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隐私敏感环境的重新分解，以建立联结树代理组织

分散概率推理，约束推理和决策理论推理的许多框架都采用结点树代理组织（JT-org）。Agent环境的自然分解可能不允许JT-org。因此，JT-org的构建涉及三个相关任务：（1）识别对于给定的环境分解是否存在JT-org。（2）如果存在JT-org，则构造一个。（3）当不存在JT-org时，修改环境分解以使其存在，然后构造它。任务3需要重新分解环境。但是，重新分解会导致JT-org链接的隐私丢失，包括代理程序隐私，拓扑隐私，私有变量的隐私和共享变量的隐私。我们提出了一种新颖的算法套件，即分布式代理环境重新分解（DAER），它可以分布式地完成所有三个任务。对于任务1和2，DAER不会损失JT-org链接的隐私。对于任务3，与现有的JT-org构建方法相比，它带来的隐私损失明显更少。对DAER的性能进行了正式分析和经验评估。