The Princeton-Yahoo! machine learning speaker series continues TODAY with Michael Littman from Rutgers University. Michael is the director of the Rutgers Laboratory for Real-Life Reinforcement Learning (RL^3) and his research in machine learning examines algorithms for decision making under uncertainty. Michael will be giving a talk TODAY, at 4:25pm, in CS 105 (small auditorium). The title and abstract for his talk are included below and can also be found on the website for the Princeton-Yahoo! speaker series: http://ai.cs.princeton.edu/pmwiki/pmwiki.php?n=Public.Talks. --- Efficiently Learning to Behave Efficiently Michael Littman Rutgers University 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 learning-theory 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.