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

易处理的 NAT 模型贝叶斯网络和代理组织的隐私敏感构建

• 批准号: RGPIN-2017-03715
• 负责人: Xiang, Yang
• 金额: $ 1.46万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710405
• 关键词: Tractable; NAT; Modeled; Bayesian; Networks

(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.

(1)在个人和移动的设备中，经常使用僵化、简单化的规则进行决策。例如，电话号码可能由于来自其的垃圾电话的报告而被置于黑名单上，从而导致来自该号码的未来呼叫被过滤。该规则忽略了报告本身可能是垃圾邮件的可能性，从而导致不受欢迎的操作。贝叶斯网络（BN），基于知识的系统，能够权衡复杂的上下文信息，可以帮助用户更智能的决策。由于一般BN中的推理是棘手的，因此有必要识别能够进行有效推理的BN的子类。它们包括低树宽的BN，以及高树宽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的BN。 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，并且NAT模型比基于CSI的局部结构（诸如代数决策图）更紧凑。本研究将探讨如何在NAT建模的高树宽BN中进行易处理的推理，如何通过利用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得这样的BN。它的成功将扩大易于处理的高树宽BN的子类，使BN推理更广泛地部署。 (2)合作智能系统（称为代理）非常适合应用程序，如监控复杂的设备或供应链中的协同设计。通常，代理人通过一个组织进行合作。连接树（JT）就是这样一种组织，并且被发现比经常使用的伪树优越上级。代理可以嵌入丰富的知识，例如，在设备子系统上，它是子系统供应商专有的，需要保持私有。然而，构建JT组织的常见方法遭受侵犯这种隐私。因此，供应商有可能失去知识产权。 为了改善这些代理系统中的隐私，本研究研究如何构建JT组织，如果可能的话，没有隐私损失，如果不可避免的损失最小。灵活的JT组织结构还开发了隐私保护，以处理系统组成的变化，例如，当由于系统扩展而添加代理时。将研究完全自主、隐私保护JT构建的可行性，例如，而不使用外部指定的领导者代理。这项研究的成功完成将关闭在JT组织上建立的代理系统中的隐私丢失的漏洞。强大的隐私保证，加上JT组织的其他上级计算属性，将使这些代理系统具有更广泛的适用性。