Medium 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. Application domains include robot soccer, autonomous bidding agents, and autonomous traffic management. 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. He has developed teams of robot soccer agents that have won ten robot soccer tournaments (RoboCup) in both simulation and with real robots. He has also developed agents that have won ten auction trading agent competitions (TAC). Prof. Stone is the author of "Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer" (MIT Press, 2000) as well as a co-author of "Autonomous Bidding Agents: Strategies and Lessons from the Trading Agent Competition" (MIT Press, 2007).

Prof. Stone 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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