Human Intelligence Synergy and Swarm Intelligence Evolution Forum
August 22 (Friday) 1:00 PM - 3:00 PM
Location: Nanjing Hall, 3rd Floor

Introduction:
       Li Xiaoping, Dean of the School of Computer Science at Guangdong University of Technology, is a National High-Level Talent, New Century Excellent Talent of the Ministry of Education, Distinguished Professor of the University's Hundred Talents Program, Second-Level Professor, Doctoral Supervisor, Project Lead of a National Key R&D Program, Distinguished Member of CCF, Senior Member of IEEE, Deputy Director of the CCF Collaborative Computing Committee, and Standing Member of the Intelligent Simulation Optimization and Scheduling Committee of the Chinese Simulation Society. He has twice been selected for Jiangsu Province's "Six Talent Peaks" training program. He has presided over two National Key R&D Program projects, two National Key R&D Program projects, one National 863 Program project, one National Support Program project, and seven National Natural Science Foundation projects. He has also participated in one National Natural Science Foundation key project (as a co-institutional leader). He has published over 100 papers in international and domestic journals and conferences, including TC, TPDS, TSC, TKDE, TMM, TCYB, TSMC.A, TEVC, TASE, TCC, EJOR, and OMEGA. He received the Second Prize of the Jiangsu Provincial Science and Technology Award in 2022, published one monograph, obtained over 10 national invention patents, and developed three international standards. His main research interests include scheduling optimization, big data, cloud computing, service computing, intelligent manufacturing, intelligent algorithms, and complex system integration.

Report Title: Intelligent Collaborative Scheduling of Cloud Resources

Report Introduction: 

        Resource scheduling in cloud computing environments is extremely complex. Considering diverse application scenarios such as strong uncertainty, task distribution, heterogeneous resources, and diverse objectives, this paper establishes a general scheduling model determined by factors such as the scheduled tasks, resources, constraints, and objective functions, and analyzes the complexity of the corresponding scheduling problem. By incorporating artificial intelligence technology, this paper explores knowledge about task, resource, and algorithm patterns, and proposes a framework and method for intelligent collaborative scheduling that is fast, optimal, and stable. This paper introduces several classic research results on supply-demand matching and outlines promising future research in this area.

Introduction:
        Doctor of Engineering, Professor and Vice Dean of the School of Computer Science at Northwestern Polytechnical University, recipient of the National Outstanding Young Scientist Fund, Deputy Director of the Key Laboratory of Human-Machine-Object Integrated Crowd Intelligence Computing of the Ministry of Education, Deputy Director of the Key Laboratory of Intelligent Perception and Computing of the Ministry of Industry and Information Technology, and Director of the Interdisciplinary Research Center for Computing and Art at Northwestern Polytechnical University. He was selected as one of the "New Century Excellent Talents" by the Ministry of Education in 2012 and a Young Talent in the National "Thousand Talent Plan" in 2017. He is also a Highly Cited Scholar in China by Elsevier and a "2023 China Intelligent Computing Innovator" by MIT Technology Review. His research focuses on intelligent IoT and ubiquitous computing, swarm intelligence, and mobile crowd sensing. He has published over 150 papers in major domestic and international journals and conferences, including IEEE/ACM Transactions, and has published monographs such as "Human-Machine-Object Fusion Crowd Intelligence Computing" and "Introduction to the Intelligent Internet of Things." He also promotes applications and technologies in key national needs, such as smart cities and social governance. He has received the First Prize in Natural Sciences from the Ministry of Education, the First Prize in Natural Sciences from Shaanxi Province, the Second Prize in Natural Sciences from CCF, and Best Paper Awards at international conferences, including ACM SenSys'24, IEEE UIC'17, and BIBM'20. He serves on the editorial boards of leading international journals such as IEEE Transactions on Human-Machine Systems, Frontiers of Computer Science, and ACM IMWUT. Senior member of the IEEE, CCF Council member, CCF Xi'an Chapter Chair, and Deputy Director of the CICC Cognition and Behavior Committee.

Report Title: Human-Machine-Object Fusion Swarm Computing: LLM vs. Distributed Cluster Intelligence

Report Introduction: 

       Recently, with the rise of intelligent IoT, edge intelligence, and swarm intelligence, enhanced collaboration among heterogeneous intelligent agents across space—human, machine, and object—will become the most important evolutionary direction for the next generation of crowd-based perception computing—i.e., "Human-Machine-Object Fusion Swarm Computing." Human-machine-object fusion swarm computing involves the intersection of multiple disciplines, including the Internet of Things, artificial intelligence, ecology, complex systems science, and sociology. It explores the implicit connection and mapping mechanism between the interactive coordination of natural clusters and the enhanced collaboration among artificial swarm agents. Through the organic interaction, collaboration, competition, and game-playing of heterogeneous swarm agents, it aims to construct an intelligent perceptual computing space with self-organization, self-learning, self-adaptation, and continuous evolution capabilities. This report will explore the fundamental theories, scientific challenges, and key technologies of human-machine-object fusion swarm computing and introduce our research progress in this area.

Introduction:
      Professor, Doctoral Supervisor, and Vice Dean of the School of Computer Science and Technology at Fudan University, Deputy Director of the Shanghai Key Laboratory of Data Science, Director of the Center for Social Computing Research at Fudan University, and Visiting Scholar at Carnegie Mellon University (CMU). He is currently a Distinguished Member of the China Computer Federation, Secretary-General of the Collaborative Computing Committee, and Vice Director of the Collaborative Information Services Committee of the Shanghai Computer Federation. His research interests include CSCW and social computing, human-intelligence collaboration and interaction, large-scale intelligent agent simulation and deduction, domain-wide large-scale models and applications, crowd-intelligence collaboration and systems, and intelligent governance of digital societies. As project leader, he has overseen numerous National Natural Science Foundation projects, Ministry of Science and Technology Key R&D Program projects, 863 projects, and Shanghai Municipal projects. His research has been published in leading conferences and journals in the field, including UbiComp, CSCW, CHI, NeurIPS, WWW, SIGIR, IEEE TKDE, and ACM TOIS. He has received five Best Paper (nominated) awards, including at CSCW’15 and CSCW’18. He regularly serves as an AC for CCF Category A conferences, including CSCW and CHI, serves as program committee (co-)chair for numerous renowned academic conferences both domestically and internationally, and serves as associate editor and editorial board member for numerous academic journals both domestically and internationally.

Report Title: From Crowd Intelligence Collaboration to Human-AI Collaboration: A Human-Centered Computing Perspective

Report Introduction:

        In the open internet environment, large numbers of users organize themselves into groups or communities, leveraging the convergence of collective intelligence to collaboratively process large-scale, complex tasks. This has led to the emergence of crowd intelligence collaborative computing, a collaborative work model. With the rise of generative AI, particularly large-scale model technologies, crowd intelligence collaborative computing is evolving into human-AI collaborative computing, where humans and AI interact and collaborate, leveraging their complementary strengths through information exchange, knowledge transfer, and intent understanding to collaboratively complete complex tasks. Human-intelligence collaborative computing not only escalates the complexity of common issues in crowd-intelligence collaborative computing, such as team building and role organization, task division, and result aggregation, but also presents new challenges, such as personalized human-intelligence information transmission, trust enhancement, meaning construction, emotional optimization, and value alignment. This report will address these challenges from the unique perspective of human-centered computing, introduce the research team's latest research in human-intelligence collaborative computing, and look forward to the development trends of human-intelligence collaborative computing in the era of large models.

Introduction:
      Distinguished Professor, Second-Level Professor, and Doctoral Supervisor at Southeast University; Member of the Expert Group for the Development of the National Key R&D Program Guidelines; Chief Scientist for two consecutive National Key R&D Program projects; Member of the Expert Group for the DAMO Academy Qingcheng Award Review Committee; and Member of the National Intelligent Computing Standardization Working Group. Recipient of the Ministry of Education's New Century Excellent Talent Award, the first Jiangsu Province Outstanding Youth Fund, the Jiangsu Province High-Level Talent Program ("333 Project") Young and Middle-Aged Scientific and Technological Leader Award, a nominee for the National Outstanding Doctoral Dissertation Award, and a recipient of Jiangsu Province's Six Talent Summits. Distinguished Member of the China Computer Federation, Senior Member of the IEEE.

Report Title: Swarm Intelligence in the Collaborative Ecosystem of Industrial Chains

Report Introduction:

        Traditional industrial chains often adopt a centralized, vertical model dominated by the leader enterprise. Resource allocation and information flow between upstream and downstream enterprises in the industry chain rely heavily on the decisions of the leader enterprise, leading to problems such as information asymmetry, uneven resource distribution, and insufficient innovation. The emergence of third-party industrial chain platforms has effectively alleviated this situation. By bringing together multiple enterprise groups across various industrial chains, they have formed a complex and diverse ecosystem. The interaction and collaboration between these enterprise groups has given rise to swarm intelligence, bringing new impetus to the development of the industrial chain. Therefore, based on this third-party-operated industrial chain collaboration platform, we studied the collaborative model and swarm intelligence emergence mechanism of industrial chain collaboration enterprise groups, as well as the swarm intelligence model and data architecture. Based on the researched crowd-intelligence-driven model, we developed a cluster of industrial chain group intelligent service components and a crowd-intelligence-driven multi-industry chain collaboration platform, which has been applied in various industrial chains, including BYD Auto, Xiaoya Home Appliances, and the Jinan Industrial Development Park.

About:
        Professor and doctoral supervisor at the School of Artificial Intelligence, Nankai University, IEEE He is a Fellow of the IEEE Computational Intelligence Society, recipient of the IEEE Young Scholar Award, a Young Yangtze River Scholar of the Ministry of Education, a National Excellent Young Scholar, and the Wu Wenjun Outstanding Young Scholar Award in Artificial Intelligence. He is also a Clarivate Analytics Highly Cited Researcher, one of the top 2% of scientists in the field of artificial intelligence (listed in both the annual scientific impact and lifetime scientific impact lists), and a Highly Cited Researcher in China for 10 consecutive years from 2014 to 2023. His research focuses on artificial intelligence, evolutionary computation, swarm intelligence, and their applications. He serves as Associate Editor of the leading international academic journals in evolutionary computation, artificial intelligence, and control, including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Transactions on Systems, Man, and Cybernetics: Systems, and IEEE Transactions on Artificial Intelligence.

Report Title: Evolutionary Optimization Driven by Dual Cognitive and Behavioral Intelligence

Report Introduction:

        Learning and optimization are two key human skills. Learning acquires cognitive intelligence, while optimization achieves behavioral intelligence. These are also two important ways for artificial intelligence to emulate human intelligence, resulting in a series of knowledge learning algorithms and behavioral optimization algorithms. This report will explore the synergy between learning and evolutionary algorithms in artificial intelligence, focusing on how to achieve high-level understanding of evolutionary behavior through knowledge learning. It will also combine cognitive intelligence and behavioral intelligence to support evolutionary optimization algorithms through dual intelligence. Furthermore, the report will explore the theory and design of evolutionary optimization algorithms driven by dual intelligence, knowledge learning and behavioral evolution, for solving complex optimization problems. Through knowledge learning and behavioral evolution, a new AI algorithm that integrates knowledge and action is realized, thereby improving the optimization and intelligence level of AI, providing new solutions to modern, ultra-complex optimization problems, and promoting new developments in AI.