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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750397
• 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）严格，简单的规则通常用于个人和移动设备中的决策。例如，由于报告了垃圾邮件调用的报告，可以将电话号码放在黑色列表上，从而导致未来的电话被过滤。该规则忽略了报告本身可能是垃圾邮件的可能性，导致不良行动。贝叶斯网络（BNS），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智能的决策。由于BNS中的推论是棘手的，因此有必要识别能够有效推断的BN的子类。 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,例如代数决策图。这项研究将研究如何在具有高树宽的NAT模型BN中进行可探讨的推断，如何通过利用CSI和可表达的ICI来进一步提高推理效率，以及如何通过机器学习获得此类BN。它的成功将扩大高树宽的可拖动BN的子类，从而使BN推断更广泛地可部署。 （2）合作智能系统（称为代理）非常适合在供应链中监视复杂设备或协作设计等应用。通常，代理商通过组织合作。接线树（JT）就是这样的组织，被发现比经常使用的伪tree优越。代理可以将丰富的知识（例如，在设备子系统）中嵌入，该系统是子系统供应商专有的，需要保持私密。但是，构建JT组织的常见方法遭受了这种隐私的侵犯。结果，供应商有可能失去智力性能。为了改善这些代理系统的隐私性，本研究研究了如何在不可避免的情况下构建JT组织而无需隐私损失，如果不可避免。灵活的JT组织构建还具有隐私保护，以处理系统组成的变化，例如，由于系统扩展而添加了代理。将研究完全自主，保护JT建设的可行性，例如，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基于JT组织的代理系统的隐私损失的循环漏洞。强大的隐私保证，再加上JT组织的其他卓越计算特性，将使这些代理系统更加广泛地适用。