Machine Learning and Multiagent Systems: From robot soccer to autonomous traffic
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The Learning Agents Research Group at UT Austin uses robot soccer to study Artificial Intelligence (AI) and Robotics. Our research focuses on enabling autonomous Aibo four-legged robots to improve their playing ability from experience (machine learning), and to interact with one another, both collaboratively and adversarially (multiagent systems). Our robots function completely autonomously: they sense their environment; engage in high-level cognitive decision-making; and then execute their actions in the environment. This talk provides an overview of our robot soccer research, and presents how it relates to autonomous bidding agents, autonomic computing, and traffic management.
Dr. Peter Stone is an Alfred P. Sloan Research Fellow and Associate Professor in the Department of Computer Sciences at the University of Texas at Austin. He 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. Peter's research interests include machine learning, multiagent systems, robotics, and e-commerce. In 2003, he won a CAREER award from the National Science Foundation for his research on learning agents in dynamic, collaborative, and adversarial multiagent environments. In 2004, he was named an ONR Young Investigator for his research on machine learning on physical robots. Most recently, he was awarded the
prestigious IJCAI 2007 Computers and Thought award.