Multiagent Learning: Foundations and Recent Trends

Tutorial at IJCAI-17
Saturday, August 19, 2017
Melbourne, Australia


Interaction between multiple autonomous agents is a core area of research in AI. The ability to learn how to interact from experience has emerged as a useful paradigm to handle scenarios in which elements of the environment are unknown beforehand (such as the behaviours of other agents) or which are too complex to be solved by decision-theoretic planners. This half-day tutorial will provide a comprehensive introduction to multiagent learning, including foundational work in game theory and different methodologies developed in AI research, as well as recent trends and open problems.

The tutorial is designed to appeal to both beginners and researchers familiar with multiagent learning. Beginners will benefit from a systematic overview of the field while experienced researchers can freshen up their knowledge and may learn about work they weren't previously familiar with. The tutorial requires no prior knowledge of multiagent systems but assumes familiarity with basic probability and statistics.

Talk structure and slides will be posted soon.


Dr. Stefano V. Albrecht is a postdoctoral fellow in the Department of Computer Science at The University of Texas at Austin, where his research is supported by a Feodor Lynen Research Fellowship from the Alexander von Humboldt Foundation. His research interests are in the areas of autonomous agents, multiagent systems, machine learning, and game theory, with a focus on sequential decision making under uncertainty. Dr. Albrecht completed his Ph.D. in 2015 at The University of Edinburgh. His research on multiagent learning and probabilistic inference has been published in leading AI conferences and journals, including AAAI, UAI, AAMAS, AIJ, and JAIR. Dr. Albrecht is a co-editor of the Special Issue on Multiagent Interaction without Prior Coordination in the Journal of Autonomous Agents and Multi-Agent Systems.

Dr. 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, 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.