Long Bio

Dr. Peter Stone is the David Bruton, Jr. Centennial Professor of Computer Science at the University of Texas at Austin. He received his Ph.D. in 1998 and his M.S. in 1995 from Carnegie Mellon University, both in Computer Science. He received his B.S. in Mathematics from the University of Chicago in 1993. From 1999 to 2002 he was a Senior Technical Staff Member in the Artificial Intelligence Principles Research Department at AT&T Labs - Research.

Prof. Stone's research interests in Artificial Intelligence include planning, machine learning, multiagent systems, robotics, and e-commerce. His doctoral thesis research contributed a flexible multiagent team structure and multiagent machine learning techniques for teams operating in real-time noisy environments in the presence of both teammates and adversaries. His long-term research goal is to create complete, robust, autonomous agents that can learn to interact with other intelligent agents in a wide range of complex, dynamic environments.

Prof. Stone is currently continuing his investigation of machine learning, multiagent learning, and robotics at UT Austin. Application domains include robot soccer, autonomous bidding agents for auctions, and autonomous traffic management. Within the robot soccer domain, he is studying multiagent techniques in reinforcement learning, specifically temporal difference learning, for learning successful policies by a team of cooperating agents. In the context of auctions, he is investigating adaptive bidding policies that are applicable for simultaneous multi-round auctions involving interacting goods. In autonomous intersection management he has developed a novel protocol by which autonomous vehicles can traverse intersections with 2 orders of magnitude less delay than is possible with traffic signals or stop signs.

Prof. Stone is a vice president of the international RoboCup Federation, was a co-chair of RoboCup-2001 at IJCAI-01, was a Program Co-Chair of AAAI 2014 and AAMAS 2006 and was General Co-Chair of AAMAS 2011. He has developed teams of robot soccer agents that have won RoboCup championships in the simulation (1998, 1999, 2003, 2005, 2011, 2012, 2014), standard platform (2012), and small-wheeled robot (1997, 1998) leagues. He led tutorials on robot soccer at AAAI-99, Agents-99, and IJCAI-99 and on autonomous bidding agents (AAMAS-07 and AAAI-07). He has also developed agents that have won auction trading agents competitions (2000, 2001, 2003, 2005, 2006, 2008, 2009, 2010, 2011, 2013). Peter has served on various program committees and has co-chaired workshops on learning agents (at Agents-2000, Agents-2001, and the AAAI Spring Symposium in 2002) and on RoboCup (at RoboCup-2000).

Prof. Stone is the author of "Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer" (MIT Press, 2000), co-author of "Autonomous Bidding Agents: Strategies and Lessons from the Trading Agent Competition" (MIT Press, 2007), and "Intelligent Autonomous Robotics" (Morgan & Claypool, 2007) as well as an author of many technical papers in conferences and journals.

Prof. Stone won best-paper awards at the International Conference on Social Robotics (ICSR) in 2013, the RoboCup Symposium in 2007, at the Genetic and Evolutionary Computation Conference (GECCO) in 2006, and at the Agents-2001 conference. Prof. Stone was awarded the Allen Newell Medal for Excellence in Research in 1997. He is an Alfred P. Sloan Research Fellow, Guggenheim Fellow, AAAI Fellow, Fulbright Scholar, and 2004 ONR Young Investigator. 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. In 2003, he won an NSF CAREER award for his proposed long term research on learning agents in dynamic, collaborative, and adversarial multiagent environments, and in 2007 he received the prestigious IJCAI Computers and Thought Award, given biannually to the top AI researcher under the age of 35.
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