Edward Tunstel is CTO of Motiv Space Systems, Inc., a space and ground robotics company. He was previously with the Autonomous & Intelligent Systems Department at Raytheon Technologies Research Center, USA, during 2017-2021 where he provided leadership, expertise, and associated strategy development and led a research group focused on technologies enabling autonomy and human-collaborative capabilities for manufacturing and service applications. During the prior decade, he was with the Johns Hopkins Applied Physics Laboratory (APL) as a senior roboticist in its research department and Intelligent Systems Center, and as space robotics & autonomous control lead in its space department. At APL he was engaged in modular open systems development efforts supporting advanced EOD robotic systems programs as well as robotics and autonomy research for future national security and space applications. Prior to APL he was with NASA JPL for close to two decades, where he was a senior robotics engineer and group leader of its Advanced Robotic Controls Group. He worked on the NASA Mars Exploration Rovers mission as both a flight systems engineer responsible for autonomous navigation and associated V&V, and as rover engineering team lead for mobility and robotic arm subsystems during surface mission operations on Mars.

Dr. Tunstel is the Jr. Past President (2020-2021) of the IEEE SMC Society and an IEEE Fellow with over 170 technical publications including five co-edited/authored books in his areas of research interest, which include mobile robot navigation, autonomous control, cooperative & human-collaborative robotics, robotic systems engineering, and applications of soft computing to autonomous systems.

Abstract: Cyber-physical social intelligence research is setting a stage that fosters convergence of a range of technologies with implications for many application domains. Impacts on advanced robotics should be anticipated that would enable robots to be applied in a variety of ways as partners to humans in multiple settings including smart factories, logistics environments, domestic/office environments, disaster scenes, and exploration outposts, to name a few. This talk discusses technologies and interaction approaches requiring advancement and further attention toward enabling smart human-collaborative robots that are responsive to multiple modes of high-level, intuitive human interaction. Also discussed is the need to increase robotic intelligence and leverage cognitive architectures to reach a capacity to interpret high-level commands and situations, and to autonomously execute or behave accordingly. The talk further expounds on the potential to move beyond such capabilities for individual robots to a next-level of shared robotic intelligence by leveraging concepts from memetics. In that regard, a motivating objective would be to enable transmission of intelligence (behaviors, knowledge, skills) among robots and from human partners as well as the notion of communal spread of robot memes toward a form of shared social intelligence. Beyond conveying a sense for technologies that could enable robots to be more human-collaborative and multi-functional as partners in the real-world, an aim is to invite consideration of how the cyber-physical social intelligence techniques could be applied in such a context.

Dr. Derong Liu is a Fellow of the IEEE, a Fellow of the International Neural Network Society, a Fellow of the International Association of Pattern Recognition, and a Member of Academia Europaea (The Academy of Europe). He received the PhD degree in electrical engineering from the University of Notre Dame, USA, in 1994. He became a Full Professor of Electrical and Computer Engineering and of Computer Science at the University of Illinois at Chicago in 2006. He was selected for the “100 Talents Program” by the Chinese Academy of Sciences in 2008, and he served as the Associate Director of The State Key Laboratory of Management and Control for Complex Systems at the Institute of Automation, from 2010 to 2016. He has published 19 books. He is the Editor-in-Chief of Artificial Intelligence Review (Springer). He was the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems from 2010 to 2105.

Abstract: Researchers have been searching for novel control methods to handle the complexity of modern industrial processes. Artificial intelligence and especially machine learning approaches might provide a solution for the next generation of control methodologies that can handle the level of complexities in many modern industrial processes. It has been shown by many researchers reinforcement learning can do a very good job approximating optimal control actions and provide a nearly optimal solution for the control of complex nonlinear systems. It requires a combination of function approximation structures such as neural networks and optimal control techniques such as dynamic programming. Theoretical development has been on a fast-track in the past ten years. On the other hand, parallel control, cloudcontrol, as well as agent-based control have been studied as alternatives for handling complex nonlinear systems. This lecture will review the development of these methodologies to summarize the inherent relationship among these developments.

Dr. Bin Hu is Professor and Director of Gansu Provincial Key Laboratory of Wearable Computing at Lanzhou University and Adjunct Professor of Computing Department at Open University. His research areas focus on affective computing and computational psychophysiology. He is currently an Institution of Engineering and Technology (IET) fellow, and a State Specially Recruited Experts of China. He is Chair of TC Computational Psychophysiology at IEEE SMC and Vice-Chair on the China Committee of International Society for Social Neuroscience. He is also currently serving as a member of the Steering Committee of Computer Science and Technology at the Chinese Ministry of Education. He has been PI of many key research projects funded by European FP7, National Key Research and Development Program of China, and National Basic Research Program of China (former 973 Program). He has published more than 330 research articles and been granted over a dozen patents. He was a recipient of many research awards, including the 2014 China Overseas Innovation Talent Award, the 2016 Chinese Ministry of Education Technology Invention Award, the 2018 Chinese National Technology Invention Award, and the 2019 Chinese National Invention Patent Gold Award. He was Chair or Steering Committee Member of many international conferences. He was the Guest Editor of Science supplement “Advances in Computational Psychophysiology” by the American Association for the Advancement of Science. He is currently Editor-in-Chief of IEEE Transaction on Computational Social Systems and Associate Editor of IEEE Transaction on Affective Computing.

Abstract: Computational psychophysiology is a new direction that broadens the field of psychophysiology by allowing for the identification and integration of multimodal signals to test specific models of mental states and psychological processes. Additionally, such approaches allows for the extraction of multiple signals from large-scale multidimensional data, with a greater ability to differentiate signals embedded in background noise. Further, these approaches allows for a better understanding of the complex psychophysiological processes underlying brain disorders such as autism spectrum disorder, depression, and anxiety. Given the widely acknowledged limitations of psychiatric nosology and the limited treatment options available, new computational models may provide the basis for a multidimensional diagnostic system and potentially new treatment approaches

Dr. Haibin Zhu is a Full Professor and the Coordinator of the Computer Science Program and Founding Director of Collaborative Systems Laboratory, Nipissing University, Canada. He received B.S. degree in computer engineering from the Institute of Engineering and Technology, China (1983), and M.S. (1988) and Ph.D. (1997) degrees in computer science from the National University of Defense Technology (NUDT), China. His research interests include Collaboration Theory, Technologies, Systems, and Applications, Human-Machine Systems, CSCW (Computer-Supported Cooperative Work), Multi-Agent Systems, Software Engineering, and Distributed Intelligent Systems. He has published (and been accepted) over 200 research works including 30 IEEE Transactions articles, six books, five book chapters, three journal issues, and four conference proceedings. He is a senior member of ACM, a full member of Sigma Xi, a senior member of IEEE, and a life member of CAST (Chinese Association of Science and Technology, USA). Dr. Zhu is serving as associate vice president (AVP)，Systems Science and Engineering, co-chair of the technical committee of Distributed Intelligent Systems member of the SSE Technical Activity Committee, the Conferences and Meetings Committee, and the Electronic Communications Subcommittee of IEEE Systems, Man and Cybernetics (SMC) Society, Associate Editor (AE) of IEEE Transactions on SMC: Systems, IEEE Transactions on Computational Social Systems, IEEE SMC Magazine, and IEEE Canada Review. He has been an active organizer for many conferences. He is the receipt of the meritorious service award from IEEE SMC Society (2018), the chancellor’s award for excellence in research (2011) and two research achievement awards from Nipissing University (2006, 2012), the IBM Eclipse Innovation Grant Awards(2004, 2005), the Best Paper Award from the 11th ISPE Int’l Conf. on Concurrent Engineering (ISPE/CE2004), the Educator’s Fellowship of OOPSLA’03, a 2nd class National Award for Education Achievement (1997), and three 1st Class Ministerial Research Achievement Awards from China (1997, 1994, and 1991).

Dr. Zhu is the founding researcher of Role-Based Collaboration and Adaptive Collaboration and the creator of the E-CARGO model. He has offered over 70 invited talks including keynote and plenary speeches on related topics internationally, e.g., Canada, USA, China, UK, Germany, Turkey, Hong Kong, Macau, and Singapore. His research has been being sponsored by NSERC, IBM, DRDC, and OPIC.

Abstract: Humans are social beings and people cannot live alone. Computational social simulation is a way to reproduce a real-world society and study the behavior of people in that society using computer-based systems. Computational social simulation is a long-term, cutting-edge topic in the interdisciplinary field where information technology, computer science, social science, and sociology overlap.

Role-Based Collaboration (RBC) has been proposed as a computational approach to facilitating collaboration. It utilizes the Environments – Classes, Agents, Roles, Groups, and Objects (E-CARGO) model to support collaboration by taking advantage of roles. It is divided into several phases: role negotiation, agent evaluation, role assignment, role execution, and role transfer. RBC and its related components are an abstract model, which is a perfect mapping for social activities, because Social and economic systems are typical collaboration systems.

The E-CARGO model, which has been developed into a general model for complex systems, has a good match for the requirements of computational social simulations. In this talk, we establish the fundamental requirements for social simulation and demonstrate that RBC, E-CARGO, Group Role Assignment (GRA), and Adaptive Collaboration (AC) methodologies and models are highly qualified to meet these requirements. Our continuous research on RBC and E-CARGO informs that social, political and economic phenomena can be explained by GRA, which demonstrates a collective team effort. GRA with constraints and GRA with multiple objectives can be further applied to simulate more complex phenomena in these areas. It is believed that there are numerous opportunities for research along with the presented directions. Based on E-CARGO, we present a novel approach to computational social simulation using E-CARGO related components, models, and algorithms.

This talk also illustrates several interesting case studies of computational social simulations: 1) a comparison between collectivism and individualism; 2) how to acquire the preferred position in a team of collectivism; 3) why the USA president opposes globalization; and 4) A social paradox related to the Pareto 80/20 rule. Through these case studies, E-CARGO has been verified to be a promising methodology for social simulation by competing with conventional ways.