主题: Integrated Learning and Control for Machine Intelligence主讲人: 何海波 博士地点: 松江校区2号学院楼226室时间: 2017-05-26 10:00:00组织单位: 信息科学与技术学院数字化纺织服装技术教育部工程研究中心
Brief Bio:
Haibo He is the Robert Haas Endowed Chair Professor and the Director of the Computational Intelligence and Self-Adaptive (CISA) Laboratory at the University of Rhode Island, Kingston, RI, USA. He has published one sole-author book (Wiley), edited 1 book (Wiley-IEEE) and 6 conference proceedings (Springer), and authored/co-authors over 260 peer-reviewed journal and conference papers, including several highly cited papers. He has delivered more than 50 invited talks around the globe. He was the Chair of IEEE Computational Intelligence Society (CIS) Emergent Technologies Technical Committee (ETTC) (2015) and the Chair of IEEE CIS Neural Networks Technical Committee (NNTC) (2013 and 2014). He served as the General Chair of 2014 IEEE Symposium Series on Computational Intelligence (IEEE SSCI’14, Orlando, Florida). He is currently the Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems. He was a recipient of the IEEE International Conference on Communications (ICC) “Best Paper Award” (2014), IEEE CIS “Outstanding Early Career Award” (2014), National Science Foundation “Faculty Early Career Development (CAREER) Award” (2011), and Providence Business News (PBN) “Rising Star Innovator” Award (2011). More information can be found at: http://www.ele.uri.edu/faculty/he/
Abstract:
With the recent development of machine learning and related technologies, scientists and engineers will hopefully find efficient ways to design brain-like intelligent systems that are highly robust, adaptive, scalable, and fault-tolerant to uncertain and unstructured environments. Yet, developing such truly intelligent systems requires significant research on both fundamental understanding of brain intelligence as well as complex engineering design. This talk aims to discuss the recent research developments in computational intelligence to advance the machine intelligence research and explore their wide applications in cyber physical systems (CPS) across different domains.Specifically, I will introduce a new reinforcement learning (RL) and adaptive dynamic programing (ADP) framework for improved decision-making and control capability. Compared to the existing methods with a manual or “hand-crafted” reinforcement signal design, this framework can automatically and adaptively develop the internal goal representation over time. Under this framework, I will present numerous applications ranging from smart grid control to human-robot interaction to demonstrate its broader and far-reaching applications in CPS. As a multi-disciplinary research area, I will also discuss the future research challenges and opportunities in this field. Finally, I will give a brief overview of the current status and publication information of the IEEE Transactions on Neural Networks and Learning Systems (TNNLS).