Parallel Population and Parallel Human Modelling, Analysis, and Computation: A Cyber-Physical Social Approach for Digital Twins in Metaverses

ISBN-13: 9781394181896 / Angielski

Peijun Ye; Fei-Yue Wang

Opis

Parallel Population and Parallel Human Proposes a new paradigm to investigate an individual’s cognitive deliberation in dynamic human-machine interactions 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.

Spis treści

Preface xiAcknowledgments xv1 From Behavioral Analysis to Prescription 11.1 Social Intelligence 11.2 Human-Machine Interaction 41.3 From Behavior Analysis to Prescription 61.4 Parallel Population and Parallel Human 91.5 Central Themes and Structure of this Book 11References 132 Basic Population Synthesis 172.1 Problem Statement and Data Sources 182.1.1 Cross-Classification Table 182.1.2 Sample 212.1.3 Long Table 222.2 Sample-Based Method 232.2.1 Iterative Proportional Fitting Synthetic Reconstruction 232.2.2 Combinatorial Optimization 242.2.3 Copula-Based Synthesis 262.3 Sample-Free Method 302.4 Experiment Results 372.4.1 Copula-Based Population Synthesis 372.4.2 Joint Distribution Inference 432.5 Conclusions and Discussions 52References 533 Synthetic Population with Social Relationships 553.1 Household Integration in Synthetic Population 563.2 Individual Assignment 603.2.1 Heuristic Allocation 623.2.2 Iterative Allocation 653.3 Heuristic Search 673.4 Joint Distribution Fitting 703.5 Deep Generative Models 753.6 Population Synthesis with Multi-social Relationships 783.6.1 Limitations of IPU Algorithm 783.6.2 Population with Multi-social Relationships 853.7 Conclusions and Discussions 93References 954 Architecture for Agent Decision Cycle 974.1 Parallel Humans in Human-Machine Interactive Systems 984.2 Why and What Is the Cognitive Architecture? 1004.3 Architecture for Artificial General Intelligence 1034.4 Architecture for Control 1104.5 Architecture for Knowledge Discovery 1134.6 Architecture for Computational Neuroscience 1164.7 Architecture for Pattern Recognition 1214.8 Other Representative Architecture 1234.9 TiDEC: A Two-Layered Integrated Cycle for Agent Decision 1294.10 Conclusions and Discussions 134References 1355 Evolutionary Reasoning 1415.1 Knowledge Representation 1425.2 Evolutionary Reasoning Using Causal Inference 1475.3 Learning Fitness Function from Expert Decision Chains 1565.4 Conclusions and Discussions 158References 1586 Knowledge Acquisition by Learning 1636.1 Foundation of Knowledge Repository Learning 1646.2 Knowledge Acquisition Based on Self-Supervised Learning 1676.3 Adaptive Knowledge Extraction for Data Stream 1706.3.1 Neural-Symbolic Learning 1716.3.2 Explanation of Deep Learning 1766.4 Experiment on Travel Behavior Learning 1836.5 Conclusions and Discussions 194References 1957 Agent Calibration and Validation 1997.1 Model Calibration for Agent 1997.2 Calibration Based on Optimization 2027.3 Calibration Based on Machine Learning 2097.4 Calibration Based on Cybernetics 2147.5 Calibration Using Variational Auto-Encoder 2277.6 Conclusions and Discussions 233References 2338 High-Performance Computing for Computational Deliberation Experiments 2378.1 Computational Acceleration Using High-Performance Computing 2378.1.1 Spark with Hadoop 2388.1.2 MPI/OpenMP on Supercomputing 2448.2 Computational Deliberation Experiments in Cloud Computing 2498.3 Computational Deliberation Experiments in Supercomputing 2588.4 Conclusions and Discussions 262References 2639 Interactive Strategy Prescription 2659.1 Hierarchical Behavior Prescription System 2669.2 Dynamic Community Discovery for Group Prescription 2709.3 Strategy Prescription Based on Content Match 2739.4 Active Learning in Strategy Prescription 2789.5 Conclusions and Discussions 284References 28510 Applications for Parallel Population/Human 28710.1 Population Evolution 28710.2 Computational Experiments for Travel Behavior 29210.3 Parallel Travel Behavioral Prescription 29510.4 Travel Behavioral Prescription for Sports Event 30110.5 Conclusions and Discussions 305References 30611 Ethical and Legal Issues of Parallel Population/Human 30711.1 Relationships Between the Parallel Population/Human and Its Individual Users 30811.2 Authority of the Parallel Population/Human System 30911.3 Risk Management and Responsibility Identification 31011.4 Conclusions and Discussions 311References 311Appendix A Convergence for Multivariate IPF 313Index 321

O autorze

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.