Tractable NAT-Modeled Bayesian Networks and Privacy Sensitive Construction of Agent Organizations

(1)在个人设备和移动设备中，决策通常采用僵化、简单的规则。例如，一个电话号码可能被列入黑名单，因为它报告了一个垃圾电话，导致将来从这个号码打来的电话被过滤。该规则忽略了报告本身可能是垃圾邮件的可能性，从而导致不良操作。贝叶斯网络（BNs）是一种基于知识的系统，能够权衡复杂的上下文信息，可以帮助用户做出更明智的决策。由于一般bn的推理是难以处理的，因此有必要识别能够进行有效推理的bn子类。它们包括低树宽的神经网络和高树宽的神经网络，但在局部结构中编码上下文特定独立性（CSI），并被编译成算术电路或和积网络。******在非阻碍噪声与树（NAT）模型中编码的因果影响独立性（ICI）与CSI正交，NAT模型比基于CSI的局部结构（如代数决策图）更紧凑。本研究将探讨如何对具有高树宽的nat建模的神经网络进行可处理推理，如何利用CSI和nat可表达的ICI进一步提高推理效率，以及如何通过机器学习获取这种神经网络。它的成功将扩大高树宽可处理BN的子类，使BN推理更广泛地部署。******(2)协作智能系统（称为代理）非常适合于监控复杂设备或供应链协同设计等应用。通常，代理通过组织进行合作。结树（JT）就是这样一种组织，它比常用的假树更优越。代理可以嵌入丰富的知识，例如，在设备子系统上，这是子系统供应商专有的，需要保持私有。然而，常见的JT组织构建方法都存在侵犯此类隐私的问题。因此，供应商面临着失去知识产权的风险。******为了提高这些代理系统的隐私性，本研究研究如何在可能的情况下构建没有隐私损失、在不可避免的情况下将隐私损失最小化的JT组织。开发灵活的JT组织结构，并提供隐私保护，以应对系统组成的变化，例如因系统扩展而增加代理。将研究完全自主、保护隐私的JT构建的可行性，例如，不使用外部指定的领导代理。本研究的成功完成将填补基于JT组织的代理系统中隐私丢失的漏洞。强大的隐私保证，加上JT组织的其他优越的计算特性，将使这些代理系统得到更广泛的应用**

(1) Rigid, simplistic rules are often used for decision making in personal and mobile devices. For example, a phone number may be placed on a black list, due to report of a spam call from it, causing future calls from the number to be filtered. The rule ignores the possibility that the report may itself be a spam, leading to undesirable actions. Bayesian Networks (BNs), knowledge based systems capable of weighing complex context information, can aid users with more intelligent decisions. Since inference in general BNs is intractable, it is necessary to identify subclasses of BNs that enable efficient inference. They include BNs of low treewidth, and BNs of high treewidth but encoding Context-Specific Independence (CSI) in local structures and being compiled into Arithmetic Circuits or Sum-Product Networks.******Independence of Causal Influence (ICI) encoded in Non-impeding noisy-AND Tree (NAT) models are orthoganal to CSI and NAT models are more compact than CSI based local structures, such as Algebraic Decision Diagrams. This research will investigate how to conduct tractable inference in NAT-modeled BNs with high treewidth, how to further improve inference efficiency by exploiting both CSI and NAT-expressible ICI, and how to acquire such BNs by machine learning. Its success will broaden subclasses of tractable BNs of high treewidth, making BN inference more widely deployable. ******(2) Cooperative intelligent systems (called agents) are well suited for applications such as monitoring complex equipment or collaborative design in supply chains. Often, agents cooperate through an organization. The Junction Tree (JT) is one such organization and is found superior than the often used Pseudotrees. An agent may embed rich knowledge, e.g., on an equipment subsystem, that is proprietary to the subsystem vendor and needs to remain private. However, common methods to construct JT organizations suffer from breach of such privacy. As a result, vendors risk losing intellectual properties.******To improve privacy in these agent systems, this research studies how to construct JT organizations without privacy loss if possible and with the minimum loss if unavoidable. Flexible JT organization construction is also developed with privacy protection to handle changes in system composition, e.g., when an agent is added due to system expansion. Feasibility of fully autonomous, privacy protecting JT construction will be studied, e.g., without using an externally specified leader agent. Successful completion of this research will close the loop hole for privacy loss in agent systems built on JT organizations. The strong privacy guarantee, coupled with other superior computational properties of JT organizations, will make these agent systems more widely applicable.**