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

(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，电话号码可能会被列入黑名单，因为报告了来自该号码的垃圾电话，导致来自该号码的未来电话被过滤。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，能够权衡复杂的不确定的上下文信息，可以帮助用户更智能的决策。BN编码的因果关系与图形结构量化的概率表。然而，运行BN所需的内存在每个变量的直接原因的数量n方面是指数级的。当n很大时，所需的内存可能超过智能手机或传感器的内存，限制了BN在此类设备中的部署。为了克服这一点，本研究试图将运行BN的内存需求降低到n的线性。它创新地探索了最近的建模技术，非阻碍噪声与树（NAT），近似概率表的BN。NAT模型采用n中的内存线性，并承诺比现有技术更准确地逼近BN概率表。如何最好地近似BN与NAT建模，以及如何与NAT建模BN在线性记忆的原因将被研究。它的成功将大大减少使用BN进行概率推理所需的内存，允许软件工程师在普适计算设备中部署BN，并在前所未有的应用范围内实现智能决策。(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。代理人合作往往是通过一个组织。所谓的连接树（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，关于设备组件，该组件是组件供应商专有的，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。为了改善这些智能系统中的隐私，本研究研究如何构建JT组织，如果可能的话，没有隐私损失，如果不可避免的话，损失最小。灵活的JT组织结构将设计有隐私保护，以处理系统组成的变化，例如，当添加组分及其试剂时。完全自主、隐私保护JT构建的可行性，即，没有使用外部指定的领导者代理，将被调查。这项研究的成功完成将关闭一个环洞的隐私代理系统的基础上JT组织。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。

(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.