CS Department seminar: Michael L. Littman (Brown University)
February 14, 2013
Title: Efficiently Learning to Behave Efficiently
Speaker: Michael L. Littman, (http://cs.brown.edu/~mlittman/), Brown University
Time: Thursday, February 14th, 4:00pm-5:00pm
Location: Babbio Center 210
Host: Jingrui He
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 via a framework we call KWIK (Knows What It Knows) learning. I'll summarize the framework, our algorithms, their formal validations, and their empirical evaluations in robotic and videogame testbeds. This approach holds promise for attacking challenging problems in a number of application domains.
Michael L. Littman joined Brown University's Computer Science after ten years (including 3 as chair) at Rutgers University. His research in machine learning examines algorithms for decision making under uncertainty. Littman has earned multiple awards for teaching and his research has been recognized with three best-paper awards on the topics of meta-learning for computer crossword solving, complexity analysis of planning under uncertainty, and algorithms for efficient reinforcement learning. He has served on the editorial boards for the Journal of Machine Learning Research and the Journal of Artificial Intelligence Research. He is general chair of International Conference on Machine Learning 2013 and program chair of the Association for the Advancement of Artificial Intelligence Conference 2013.