Ann Nowé is a full professor at the Vrije Universiteit Brussel, where she is heading the Artificial Intelligence lab. She graduated from the University of Ghent, as a master in Mathematics with a minor in computer science. She obtained her PhD in 1994 at the Vrije Universiteit Brussel in collaboration with Queen Mary and Westfield college London, UK. Currently, she is a full professor at the Vrije Universiteit Brussel . Her research interests include Multi-Agent Reinforcement Learning (MARL) and Multi-criteria Reinforcement Learning (MORL). Her research output includes 150+ co-authored articles in international journals and proceedings of international conferences, as well as the organization of tutorials and workshops at international conferences, including : JMLR, JAAMAS, AAMAS, AAAI, ICML and ECML. Within MARL, she focuses on the coordination of agents with limited communication, social agents learning fair policies and the relationship between Learning Automata and Evolutionary Game Theory. Within MORL she mainly looks at settings where no assumptions on the shape of the Pareto front can be made. Ann Nowé was the chairman of the BNVKI (BeNeLux association for Artificial Intelligence) and a board member of EurAi (European Association for Artificial Intelligence). She was also a member of the expert panel on Informatics and Knowledge Technology of the National Science foundation, Flanders. Currently, she is the coordinator of the research network on Guiding Networked Societies supported by the National Science foundation, Flanders.

Igor Mordatch, is a research scientist at OpenAI and faculty at Carnegie Mellon University Robotics Institute. Previously he was a post-doctoral fellow working with professor Pieter Abbeel at University of California, Berkeley and received his PhD at University of Washington and undergraduate degree in University of Toronto. He worked as a visiting researcher at Stanford University and Pixar Research. His research interests lie in the development and use of optimal control and reinforcement learning techniques for robotics.

Mac Schwager is an assistant professor of Aeronautics and Astronautics at Stanford University. He directs the Multi-robot Systems Lab (MSL) where he studies distributed algorithms for control, perception, and learning in groups of robots and autonomous systems. He is interested in a range of applications including cooperative surveillance with teams of UAVs, agile formation control and collision avoidance for UAVs, autonomous driving in traffic, cooperative robotic manipulation, and autonomous drone racing. He obtained his BS degree from Stanford, and his MS and PhD degrees from MIT. He was a postdoctoral researcher in the GRASP lab at the University of Pennsylvania, and in CSAIL at MIT. Prior to joining Stanford, he was an assistant professor at Boston University from 2012 to 2015. He received the NSF CAREER award in 2014, and has received numerous best paper awards at top-tier venues, including the IEEE Transactions on Robotics King-Sun Fu best paper award in 2016.

Mykel Kochenderfer is Assistant Professor of Aeronautics and Astronautics and Assistant Professor, by courtesy, of Computer Science at Stanford University. He is the director of the Stanford Intelligent Systems Laboratory (SISL), conducting research on advanced algorithms and analytical methods for the design of robust decision making systems. Of particular interest are systems for air traffic control, unmanned aircraft, and other aerospace applications where decisions must be made in uncertain, dynamic environments while maintaining safety and efficiency. Research at SISL focuses on efficient computational methods for deriving optimal decision strategies from high-dimensional, probabilistic problem representations. He received his Ph.D. from the University of Edinburgh and B.S. and M.S. degrees in computer science from Stanford University. He is the author of "Decision Making under Uncertainty: Theory and Application" from MIT Press (2015).

Peter Stone is the David Bruton, Jr. Centennial Professor and Associate Chair of Computer Science, as well as Chair of the Robotics Portfolio Program, at the University of Texas at Austin. In 2013 he was awarded the University of Texas System Regents' Outstanding Teaching Award and in 2014 he was inducted into the UT Austin Academy of Distinguished Teachers, earning him the title of University Distinguished Teaching Professor. Professor Stone's research interests in Artificial Intelligence include machine learning (especially reinforcement learning), multiagent systems, robotics, and e-commerce. Professor Stone received his Ph.D in Computer Science in 1998 from Carnegie Mellon University. From 1999 to 2002 he was a Senior Technical Staff Member in the Artificial Intelligence Principles Research Department at AT&T Labs - Research. He is an Alfred P. Sloan Research Fellow, Guggenheim Fellow, AAAI Fellow, IEEE Fellow, Fulbright Scholar, and 2004 ONR Young Investigator. In 2003, he won an NSF CAREER award for his proposed long term research on learning agents in dynamic, collaborative, and adversarial multiagent environments, in 2007 he received the prestigious IJCAI Computers and Thought Award, given biannually to the top AI researcher under the age of 35, and in 2016 he was awarded the ACM/SIGAI Autonomous Agents Research Award. Professor Stone co-founded Cogitai, Inc., a startup company focussed on continual learning, in 2015, and currently serves as President and COO.

Pradeep Varakantham is a Lee Kong Chian Fellow and an Associate Professor in the School of Information Systems at Singapore Management University. Prior to his current position, Pradeep received his PhD from University of Southern California and was a post-doctoral fellow at Carnegie Mellon University. His research is at the intersection of Artificial Intelligence, Operations Research and Machine Learning with specific focus on solving sequential matching problems in many urban environments including transportation, safety and security, and leisure. Pradeep has published research papers in top tier conferences (AAAI, IJCAI, AAMAS, ICAPS, UAI, NIPS) and journals (JAIR, JAAMAS) in Artificial Intelligence and Machine Learning. Pradeep was invited to give an early career spotlight talk at IJCAI 2016. One of his papers was nominated for best student paper award at AAMAS 2009 and he currently serves on the board of directors for IFAAMAS (governing body of AAMAS). He was finalist for the best senior program committee member award at AAMAS 2013.