Space Efficient Probabilistic Graphical Models and Privacy Sensitive Construction of Agent Organizations

代理组织的空间高效概率图形模型和隐私敏感构建

• 批准号: RGPIN-2016-03616
• 负责人: Xiang, Yang
• 金额: $ 1.6万
• 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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=614498
• 关键词: Space; Efficient; Probabilistic; Graphical; Models

(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 the 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 uncertain context information, can aid users with more intelligent decisions. BNs encode causal relations with graphical structures quantified by probability tables. However, memory needed to run BNs is exponential in the number n of direct causes per variable. When n is large, the required memory may exceed that of a smart phone or sensor, limiting deployment of BNs in such devices.To overcome that, this research seeks to reduce memory requirement for running BNs to being linear in n. It explores innovatively a recent modeling technique, Non-impeding noisy-AND Tree (NAT), to approximate probability tables in BNs. NAT models take the memory linear in n and promise to approximate BN probability tables more accurately than existing techniques. How to best approximate BNs with NAT modeling and how to reason with NAT modeled BNs within linear memory will be investigated. Its success will dramatically reduce memory required for probabilistic reasoning with BNs, allow software engineers to deploy BNs in pervasive computing devices, and enable intelligent decisions in an unprecedented range of applications. (2) Cooperative intelligent systems (called agents) are well suited for applications such as monitoring complex equipment or collaborative design in supply chains. Agent cooperation is often through an organization. The so-called Junction Tree (JT) is one such organization and is found superior than the often used pseudotrees. An agent can embed rich knowledge, e.g., about an equipment component, that is proprietary to component vendor and needs to remain private. However, common methods to construct JT organizations suffer from breach of such privacy. As a result, vendors run the risk of losing intellectual properties.To improve privacy in these intelligent 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 will be devised with privacy protection to handle changes in system composition, e.g., when a component and its agent are added. Feasibility of fully autonomous, privacy protecting JT construction, i.e., without using an externally specified leader agent, will be investigated. Successful completion of this research will close a loop hole on privacy in agent systems based 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，能够权衡复杂的不确定上下文信息，可以帮助用户做出更聪明的决策。 BNS用概率表定量的图形结构编码因果关系。但是，在每个变量的直接原因的数字n中，运行BNS所需的内存是指数的。当n很大时，所需的内存可能会超过智能手机或传感器的内存，从而限制了在这种设备中的BNS部署。为了克服，本研究试图减少运行BNS在n中线性的内存要求。它创新的近期建模技术，即非阻碍嘈杂和树（NAT），以近似BNS中的概率表。 NAT模型将内存线性在n中以n为单位，并承诺比现有技术更准确地近似BN概率表。将研究如何使用NAT建模最佳近似BN，以及如何在线性内存中使用NAT建模的BN进行推理。它的成功将大大减少BNS概率推理所需的内存，允许软件工程师在普遍的计算设备中部署BNS，并在空前的应用程序范围内实现智能决策。 （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。代理合作通常是通过组织。所谓的交界树（JT）就是这样的组织，被发现比经常使用的伪鸟优越。代理可以嵌入丰富的知识，例如，关于设备组件，该设备组件是组件供应商专有的，并且需要保持私密。但是，构建JT组织的常见方法遭受了这种隐私的侵犯。结果，供应商有失去知识产权的风险。为了改善这些智能系统的隐私，本研究研究了如何在不可避免的情况下构建无隐私损失的JT组织，如果不可避免的话，则最小损失。灵活的JT组织构建将使用隐私保护设计，以处理系统组成的变化，例如，当添加组件及其代理时。将研究完全自主，保护JT构建的隐私的可行性，即不使用外部指定的领导者代理人。这项研究的成功完成将弥合基于JT组织的代理系统中隐私的循环漏洞。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。