Reference number : GC-2017-Q1-01436, amendment number : , agreement type : G, recipient legal name : Xiang, Yang (University of Guelph), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : (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., program purpose french : (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., agreement title english : Tractable NAT-Modeled Bayesian Networks and Privacy Sensitive Construction of Agent Organizations, agreement title french : Tractable NAT-Modeled Bayesian Networks and Privacy Sensitive Construction of Agent Organizations, agreement number : RGPIN, agreement value : 100000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada