Cognitive Aware Smart Service Composition

Internet正在成为承载异构服务的全球基础设施。一般来说，在互联网上运行的服务有两种类型：(1)网络服务（例如网上商店）由服务器托管，并通过与其他网络服务通信向用户提供软件功能；(2)直接与硬件设备（如恒温器）通信并对环境产生直接影响（如改变室温）的物联网（IoT）服务（如恒温器应用程序）。尽管越来越多地使用Internet上的服务来支持人类活动，但大多数在线活动都是手动执行的。促进人与机器之间交互的智能支持有限。**在这个项目中，我们与IBM加拿大的工业合作者合作，构建一个智能服务组合框架，该框架可以感知最终用户的情景需求，自动支持最终用户执行在线任务，减少人类的认知负担。我们的目标是利用机器学习算法来学习最终用户的在线活动和偏好，以改进我们的服务推荐。拟建项目将重点关注三个方面：1)构建智能个人代理框架，将传统web服务和物联网服务无缝集成，使代理能够代表最终用户执行在线任务；2)创建分析和预测引擎，跟踪各种信息源，分析信息，并向最终用户推荐服务；3)开发多代理协作基础设施，允许不同角色的个人代理代表其最终用户进行通信和交互，以实现共同目标。**我们提议的项目的结果可以允许最终用户创建智能环境，以响应他们的需求，并帮助改善他们在各个领域的生活质量，如电子医疗、电子商务、金融服务和辅助生活。该项目将在软件工程的重要领域培养10名HQP（2名博士，4名硕士和4名学士），特别是SOA和物联网软件。**********

The Internet is becoming a global infrastructure to host heterogeneous services. Generally speaking, there are two types of services running on the Internet: (1) web services (e.g., on-line stores) are hosted by servers and provide users with software functionality by communicating with other web services; and (2) Internet of Things (IoT) services (e.g., thermostat apps) that directly communicate with hardware devices (e.g., thermostat) and make a direct impact to the environment (e.g., changing the room temperature). Despite the increasing use of the services on the Internet to support human activities, most on-line activities are performed manually. There is limited intelligent support to facilitate the interactions between human and machines.** In this project, we collaborate with our industrial collaborators in IBM Canada to build a smart service composition framework that can perceive end-users' situational needs, automatically support end-users to conduct on-line tasks and reduce human cognitive burden. We aim to utilize machine learning algorithms to learn end-users' on-line activities and preferences over time to improve our service recommendation. The proposed project will focus on three aspects:1) Building a smart personal agent framework that can seamlessly integrate the traditional web services and IoT services, and enable an agent to act on behalf of an end-user in performing on-line tasks; 2) Creating analytics and predictive engines that track various information sources, analyze the information, and recommend services to end-users; and 3) Developing a multi-agents collaboration infrastructure that allows personal agents of various roles to communicate and interact on behalf of their end-users to achieve a common goal. ** The results of our proposed project can allow end-users to create smart environments to be responsive to their needs and help improve the quality of their lives in various domains, such as e-health, e-commerce, financial services, and assisted living. This project will train 10 HQP (2 PhD, 4MSc, and 4 BSc) in the important area of software engineering, specifically SOA and IoT software.**********