Plenary Speakers

A unifying optimization framework will be presented which encompasses most commonly encountered static and dynamic cooperative multi-agent system problems, including coverage control, consensus, formation control, and persistent monitoring. One of the main challenges in this framework is ensuring that the problems can be solved through distributed algorithms where each agent requires only local information from an appropriately defined neighborhood. Another challenge arises from the fact that most interesting problems involve nonconvex objective functions allowing common gradient-based distributed algorithms to be trapped in poorly preforming local optima.
We first address the fundamental question “when can a multi-agent problem be decentralized?” in the sense that an optimal solution can be fully recovered through a distributed optimization scheme. We will show that this is possible for a large class of problems, while for others we can only achieve this in an "almost distributed" manner. We next describe a systematic method for escaping a local optimum by exploiting the structure of the objective function and knowledge of an agent’s neighborhood rather than by randomly perturbing controllable variables away from it. This is accomplished through boosting functions applied as transforms of the objective function gradient at an equilibrium point in a way that induces a search for a new equilibrium point. We will show how convergence can be attained through a distributed optimization algorithm and include examples showing how to improve solutions of some particularly difficult multi-agent problems.

Christos G. Cassandras

Boston University - USA

Christos G. Cassandras

is Distinguished Professor of Engineering at Boston University. He is Head of the Division of Systems Engineering, Professor of Electrical and Computer Engineering, and co-founder of Boston University’s Center for Information and Systems Engineering (CISE). He received a B.S. degree from Yale University, M.S.E.E from Stanford University, and S.M. and Ph.D. degrees from Harvard University. In 1982-84 he was with ITP Boston, Inc. where he worked on the design of automated manufacturing systems. In 1984-1996 he was a faculty member at the Department of Electrical and Computer Engineering, University of Massachusetts/Amherst. He specializes in the areas of discrete event and hybrid systems, cooperative control, stochastic optimization, and computer simulation, with applications to computer and sensor networks, manufacturing systems, and transportation systems. He has published over 400 refereed papers in these areas, and six books. He has guest-edited several technical journal issues and serves on several journal Editorial Boards. In addition to his academic activities, he has worked extensively with industrial organizations on various systems integration projects and the development of decision-support software. He has most recently collaborated with MathWorks, Inc. in the development of the discrete event and hybrid system simulator SimEvents.
Dr. Cassandras was Editor-in-Chief of the IEEE Transactions on Automatic Control from 1998 through 2009 and has also served as Editor for Technical Notes and Correspondence and Associate Editor. He is currently an Editor of Automatica. He was the 2012 President of the IEEE Control Systems Society (CSS). He has also served as Vice President for Publications and on the Board of Governors of the CSS, as well as on several IEEE committees, and has chaired several conferences. He has been a plenary/keynote speaker at numerous international conferences, including the American Control Conference in 2001, the IEEE Conference on Decision and Control in 2002 and 2016, and the 20th IFAC World Congress in 2017 and has also been an IEEE Distinguished Lecturer.
He is the recipient of several awards, including the 2011 IEEE Control Systems Technology Award, the Distinguished Member Award of the IEEE Control Systems Society (2006), the 1999 Harold Chestnut Prize (IFAC Best Control Engineering Textbook) for Discrete Event Systems: Modeling and Performance Analysis, a 2011 prize and a 2014 prize for the IBM/IEEE Smarter Planet Challenge competition (for a "Smart Parking" system and for the analytical engine of the Street Bump system respectively), the 2014 Engineering Distinguished Scholar Award at Boston University, several honorary professorships, a 1991 Lilly Fellowship and a 2012 Kern Fellowship. He is a member of Phi Beta Kappa and Tau Beta Pi. He is also a Fellow of the IEEE and a Fellow of the IFAC.

A unifying optimization framework will be presented which encompasses most commonly encountered static and dynamic cooperative multi-agent system problems, including coverage control, consensus, formation control, and persistent monitoring. One of the main challenges in this framework is ensuring that the problems can be solved through distributed algorithms where each agent requires only local information from an appropriately defined neighborhood. Another challenge arises from the fact that most interesting problems involve nonconvex objective functions allowing common gradient-based distributed algorithms to be trapped in poorly preforming local optima.
We first address the fundamental question “when can a multi-agent problem be decentralized?” in the sense that an optimal solution can be fully recovered through a distributed optimization scheme. We will show that this is possible for a large class of problems, while for others we can only achieve this in an "almost distributed" manner. We next describe a systematic method for escaping a local optimum by exploiting the structure of the objective function and knowledge of an agent’s neighborhood rather than by randomly perturbing controllable variables away from it. This is accomplished through boosting functions applied as transforms of the objective function gradient at an equilibrium point in a way that induces a search for a new equilibrium point. We will show how convergence can be attained through a distributed optimization algorithm and include examples showing how to improve solutions of some particularly difficult multi-agent problems.

Fei-Yue Wang

Chinese Academy of Sciences - CHINA

Fei-Yue Wang

received his Ph.D. in Computer and Systems Engineering from Rensselaer Polytechnic Institute, Troy, New York in 1990. He joined the University of Arizona in 1990 and became a Professor and Director of the Robotics and Automation Lab (RAL) and Program in Advanced Research for Complex Systems (PARCS). In 1999, he founded the Intelligent Control and Systems Engineering Center at the Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China, under the support of the Outstanding Overseas Chinese Talents Program from the State Planning Council and “100Talent Program” from CAS, and in 2002, was appointed as the Director of the Key Lab of Complex Systems and Intelligence Science, CAS. From 2006 to 2010, he was Vice President for Research, Education, and Academic Exchanges at the Institute of Automation, CAS. In 2011, he became the State Specially Appointed Expert and the Director of the State Key Laboratory for Management and Control of Complex Systems. Dr. Wang’s current research focuses on methods and applications for parallel systems, social computing, parallel intelligence and knowledge automation. He was the Founding Editor-in-Chief of the International Journal of Intelligent Control and Systems (1995-2000), Founding EiC of IEEE ITS Magazine (2006-2007), EiC of IEEE Intelligent Systems (2009-2012), and EiC of IEEE Transactions on ITS (2009-2016). Currently he is EiC of IEEE Transactions on Computational Social Systems, Founding EiC of IEEE/CAA Journal of Automatica Sinica, and Chinese Journal of Command and Control. Since 1997, he has served as General or Program Chair of more than 20 IEEE, INFORMS, ACM, and ASME conferences. He was the President of IEEE ITS Society (2005-2007), Chinese Association for Science and Technology (CAST, USA) in 2005, the American Zhu Kezhen Education Foundation (2007-2008), the Vice President of the ACM China Council (2010-2011), and the Vice President and Secretary General of Chinese Association of Automation (CAA, 2008-2018). Since 2019, he has been the President of CAA Supervision Council. Dr. Wang has been elected as Fellow of IEEE, INCOSE, IFAC, ASME, and AAAS. In 2007, he received the National Prize in Natural Sciences of China and was awarded the Outstanding Scientist by ACM for his research contributions in intelligent control and social computing. He received IEEE ITS Outstanding Application and Research Awards in 2009, 2011 and 2015, and IEEE SMC Norbert Wiener Award in 2014.

Ronald R. Yager

Iona College NY - USA

Ronald R. Yager

has worked in the area of machine intelligence for over twenty-five years. He has published over 500 papers and more then thirty books in areas related to fuzzy sets, decision-making under uncertainty and the fusion of information.
He is among the world’s top 1% most highly cited researchers with over 68,000 citations.
He was the recipient of the IEEE Computational Intelligence Society’s highly prestigious Frank Rosenblatt Award in 2016. He was the recipient of the IEEE Systems, Man and Cybernetics Society 2018 Lotfi Zadeh Pioneer Award. He was also the recipient of the IEEE Computational Intelligence Society Pioneer award in Fuzzy Systems. He received honorary doctorates from the Azerbaijan Technical University, the State University of Information Technologies, Sofia Bulgaria and the Rostov on the Don University, Russia.
Dr. Yager is a fellow of the IEEE, the New York Academy of Sciences and the Fuzzy Systems Association. He was given a lifetime achievement award by the Polish Academy of Sciences for his contributions. He served at the National Science Foundation as program director in the Information Sciences program. He was a NASA/Stanford visiting fellow and a research associate at the University of California, Berkeley. He has been a lecturer at NATO Advanced Study Institutes. He was a distinguished honorary professor at the Aalborg University Denmark. He was distinguished visiting scientist at King Saud University, Riyadh, Saudi Arabia. He received his undergraduate degree from the City College of New York and his Ph. D. from the Polytechnic University of New York. Currently, he is Director of the Machine Intelligence Institute and Professor of Information Systems at Iona College.
He is editor and chief of the International Journal of Intelligent Systems. He serves on the editorial board of numerous technology journals.