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)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，导致将来从该号码发出的呼叫被过滤。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，能够权衡复杂的不确定上下文信息，可以帮助用户做出更明智的决策。神经网络用概率表量化的图形结构来编码因果关系。然而，运行bn所需的内存在每个变量的直接原因数量上呈指数级增长。当n较大时，所需的内存可能超过智能手机或传感器的内存，从而限制了bn在此类设备中的部署。为了克服这个问题，本研究试图将运行bn的内存需求降低到线性n。它创新地探索了一种最新的建模技术，即非阻碍噪声与树（NAT），以近似bn中的概率表。NAT模型在n中采用内存线性，并承诺比现有技术更准确地近似BN概率表。如何用NAT建模最好地近似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用bp进行概率推理所需的内存，使软件工程师能够在普及计算设备中部署bp，并在前所未有的应用范围内实现智能决策。(2)协作智能系统（称为agent）非常适合于监控复杂设备或供应链协同设计等应用。代理合作通常是通过组织进行的。所谓的结树（JT）就是这样一种组织，并且被发现比经常使用的假树优越。代理可以嵌入丰富的知识，例如，关于设备组件的知识，这些知识是组件供应商专有的，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，供应商冒着失去知识产权的风险。为了提高智能系统中的隐私性，本文研究如何在可能的情况下构建无隐私损失、在不可避免的情况下将隐私损失最小化的JT组织。设计灵活的JT组织结构，保护隐私，以应对系统组成的变化，例如添加组件及其代理。将研究完全自主、保护隐私的JT构建的可行性，即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问题的漏洞。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。