Description
This book presents a comprehensive framework of scenarios engineering designed to guide and support services science in the era of modern AI. Offering a systematic overview of recent advancements, it explores key challenges and opportunities in both services science and scenarios engineering. The framework, grounded in parallel intelligence theory, addresses critical issues posed by emerging AI technologies in services systems, while providing tools for analysis, validation, and calibration. It also offers a platform for evaluating, analyzing, and comparing existing Scenarios Engineering for Services Science (SE4SS) projects, as well as guiding new research endeavors. By integrating core elements of services science, such as services systems, metrics, technology, automation, and ethics, through a multidimensional lens, the book enables researchers to explore foundational models, intelligent systems, domain-specific knowledge bases, datasets, and virtual systems. Case studies from various academic fields illustrate the application of standardized dimensions and metrics, fostering interdisciplinary insights and accelerating the development of the field.
The book is designed for researchers, practitioners, and students interested in the intersection of services science and artificial intelligence. It balances theoretical depth with practical application, offering valuable case studies and examples that clarify core concepts and make the material accessible. With its goal-oriented approach, the book is ideal for academics exploring advancements in services science and AI, data scientists applying AI to services systems, and students seeking to deepen their understanding of these rapidly evolving fields.
From Scenarios Engineering to Reliable Service Systems through ACP.- SE4SS for Intelligent Finance.- SE4SS for Intelligent Customer Services.- SE4SS for Intelligent Hotels.- SE4SS for Intelligent Cities.- SE4SS for Intelligent Logistics.- SE4SS for Intelligent Healthcare.- SE4SS for Intelligent Vehicles.
Xuan Li is an Associate Professor at the Beijing Institute of Technology, Zhuhai, China. His current research interests include parallel intelligence, scenario engineering, machine learning, and intelligent systems.
Fei-Yue Wang received his Ph.D. in Computer and Systems Engineering from Rensselaer Polytechnic Institute, Troy, New York, USA in 1990. He is the Director of the State Key Laboratory for Management and Control of Complex Systems, CASIA, the EiC of IEEE Transactions on Intelligent Vehicles and China's Journal of Intelligent Science and Technology, and the President of CAA Supervision Council. His current research focuses on methods and applications for parallel intelligence, social computing, and knowledge automation.
