Today, intelligent machines enable people to interact remotely with friends, family, romantic partners, colleagues, competitors, organizations, and others. Virtual reality (VR), augmented reality (AR), artificial intelligence (AI), mobile social media, and other technologies have been driving these interactions to an unprecedented level. As the complexity in system control and management with human participants increases, engineers are facing challenges that arise from the uncertainty of operators or users.

Parallel Population and Parallel Human: A Cyber-Physical Social Approach presents systemic solutions for modeling, analysis, computation, and management of individuals' cognition and decision-making in human-participated systems, such as the MetaVerse. With a virtual-real behavioral approach that seeks to actively prescribe user behavior through cognitive and dynamic learning, the authors present a parallel population/human model for optimal prescriptive control and management of complex systems that leverages recent advances in artificial intelligence. Throughout the book, the authors address basic theory and methodology for modeling, describe various implementation techniques, highlight potential acceleration technologies, discuss application cases from different fields, and more. In addition, the text:

Considers how an individual's behavior is formed and how to prescribe their behavioral modes
Describes agent-based computation for complex social systems based on a synthetic population from realistic individual groups
Proposes a universal algorithm applicable to a wide range of social organization types
Extends traditional cognitive modeling by utilizing a dynamic approach to investigate cognitive deliberation in highly time-variant tasks
Presents a new method that can be used for both large-scale social systems and real-time human-machine interactions without extensive experiments for modeling

Parallel Population and Parallel Human: A Cyber-Physical Social Approach is a must-read for researchers, engineers, scientists, professionals, and graduate students who work on systems engineering, human-machine interaction, cognitive computing, and artificial intelligence.

Auteur

Peijun Ye is an Associate Professor at the State Key Laboratory for Management and Control of Complex Systems with the Institute of Automation at the Chinese Academy of Sciences. His research mainly focuses on cognitive computing, artificial intelligence, computational social science and intelligent transportation systems. He is an Associate Editor for several IEEE Transactions and journals.

Fei-Yue Wang is Director of the State Key Laboratory for Management and Control of Complex Systems with the Institute of Automation at the Chinese Academy of Sciences, where he founded the Intelligent Control and Systems Engineering Center. He is an AAAS Fellow, ASME Fellow, IFAC Fellow, INCOSE Fellow, IEEE Fellow and an Outstanding Scientist of ACM. He has been serving as the Editor-in-Chief of several IEEE Transactions and journals.