One goal of Artificial Intelligence is to enable the creation of robust, fully autonomous agents that can coexist with us in the real world. Such agents will need to be able to learn, both in order to correct and circumvent their inevitable imperfections, and to keep up with a dynamically changing world. They will also need to be able to interact with one another, whether they share common goals, they pursue independent goals, or their goals are in direct conflict. This talk will present current research directions in machine learning, multiagent reasoning, and robotics, and will advocate their unification within concrete application domains. Ideally, new theoretical results in each separate area will inform practical implementations while innovations from concrete multiagent applications will drive new theoretical pursuits, and together these synergistic research approaches will lead us towards the goal of fully autonomous agents.
Dr. Peter Stone is an Alfred P. Sloan Research Fellow, Guggenheim Fellow, Fulbright Scholar, 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. In 2007, he was awarded the prestigious IJCAI 2007 Computers and Thought award, given once every two years to the top AI researcher under the age of 35.