Efficiently Learning to Behave Efficiently
Professor, Department of Computer Science
Tuesday, February 10, 2009|
4:00pm - 5:30pm
Stained-Glass Conference Room (3725 Beyster Bldg.)
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About the Event
The field of reinforcement learning is concerned with the problem of learning efficient behavior from experience. In real life applications, gathering this experience is time-consuming and possibly costly, so it is critical to derive algorithms that can learn effective behavior with bounds on the experience necessary to do so. This talk presents our successful efforts to create such algorithms within a novel machine learning framework we call "KWIK" for "knows what it knows". I'll summarize the framework, our algorithms, their formal validations, and their empirical evaluations in robotic and videogame testbeds.
Michael L. Littman directs the Rutgers Laboratory for Real Life Reinforcement Learning (RL3) and his research in machine learning examines algorithms for decision making under uncertainty. Littman worked as an Assistant Professor at Duke University, a Member of Technical Staff in AT&T's Artificial Intelligence Principles Research Department, an Adjunct Professor at Princeton University, and is now a Professor of Computer Science at Rutgers University. Both Duke and Rutgers honored him with undergraduate teaching awards and his research has been recognized with five best-paper awards on topics ranging from algorithms for efficient reinforcement learning and sequential decision making, to computational game theory, to computer crossword solving. He has served as associate editor for three of the major journals in his field.
Sponsor(s): Toyota AI Seminar Series
Open to: Public