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dc.contributor.authorZhao, Yu
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.date.accessioned2019-09-04T22:07:47Z
dc.date.available2019-09-04T22:07:47Z
dc.identifier.urihttp://hdl.handle.net/1974/26515
dc.description.abstractEnd-users face a massive amount of services when selecting and composing the desired services to meet their personal preferences. For example, people frequently use the Internet to perform on-line activities (e.g., on-line shopping and banking) and control their IoT devices (e.g., turning off home light). The ever increasing amounts of services available could be overwhelming to end-users. To relieve end-users' cognitive overloading when performing on-line activities, agents can be designed and developed to act autonomously on end-users' behalf. However, the autonomous and cooperative natures of agents make it complex to design and implement by developers. General speaking, developers face the following challenges to adopt agents for service composition: 1) difficult to program agents; 2) lack of domain knowledge to identify necessary tasks for composite services; and 3) lack of a standard interface to integrate various services (e.g., IoT devices) with web services by agents. In this thesis, we present a smart agent framework that facilitates the design and implementation of agents for service composition. More specifically, our approach consists of 5 aspects: 1) we propose an easy-to-understand semi-natural language syntax that allows developers to specify the functionalities of agents; 2) we use natural language processing techniques and machine learning algorithms to identify service related tasks from on-line sources, e.g., on-line how-to instructions; 3) we propose an approach to transform functionalities of IoT devices to web services using the standard web service infrastructure; 4) we propose a deep learning based model (i.e., DeepCont) to predict end-users' ratings on services, using user reviews and service descriptions; and 5) we use a ranking algorithm (i.e., RankBoost) to automatically learn user preferences from the service usage history, and integrate the learned user preferences to recommend a collection of services for end-users. A series of case studies demonstrate that our approaches are effective to develop agents and recommend personalized services for end-users. Our approaches relieve the required efforts from developers to design agents for personalized service composition.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesCanadian thesesen
dc.rightsQueen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canadaen
dc.rightsProQuest PhD and Master's Theses International Dissemination Agreementen
dc.rightsIntellectual Property Guidelines at Queen's Universityen
dc.rightsCopying and Preserving Your Thesisen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectmulti-agent frameworken_US
dc.subjectservice compositionen_US
dc.subjectIoT servicesen_US
dc.subjectdeep learningen_US
dc.subjectreinforcement learningen_US
dc.subjectrecommendation systemsen_US
dc.titleA Smart Agent Framework for Personalized Service Compositionen_US
dc.typethesisen
dc.description.degreeDoctor of Philosophyen_US
dc.contributor.supervisorZou, Ying
dc.contributor.departmentElectrical and Computer Engineeringen_US


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