Broad Area Colloquium For AI-Geometry-Graphics-Robotics-Vision
(CS 528)

Life-Sized Learning

Leslie Pack Kaelbling
Computer Science and Artificial Intelligence Laboratory (CSAIL)
Massachusetts Institute of Technology
Monday, May 17, 2004, 4:15PM
TCSeq 200


In the last 10 years, the combination of techniques from machine learning and operations research has allowed major advances in learning and planning for uncertain environments. Reasonably large problems can be solved using current techniques. But what if we want to scale up to the uncertain learning and planning problem that you face every day? It is many orders of magnitude larger than the biggest problem we can solve currently.

In this talk, I'll describe three early pieces of work that try to begin to address working in truly huge environments. The first is a method for learning probabilistic rules to describe naive physics models of the interactions between objects. The second is an uncertain planning algorithm that uses the rules learned by the first method to construct contingency plans that consider enough cases to perform robustly, but are much smaller than complete policies. The last piece is preliminary work on combining multiple abstraction methods dynamically, in order to allow an agent to have a working model of the environment that changes focus depending on the current situation.

About the Speaker

Leslie Pack Kaelbling is Professor of Computer Science and Engineering and a Research Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology. She has previously held positions at Brown University, the Artificial Intelligence Center of SRI International, and at Teleos Research. She received an A. B. in Philosophy in 1983 and a Ph. D. in Computer Science in 1990, both from Stanford University. Prof. Kaelbling has done substantial research on designing situated agents, mobile robotics, reinforcement learning, and decision-theoretic planning. In 2000, she founded the Journal of Machine Learning Research, a high-quality journal that is both freely available electronically as well as published in archival form; she currently serves as editor-in-chief. Prof. Kaelbling is an NSF Presidential Faculty Fellow, a former member of the AAAI Executive Council, the 1997 recipient of the IJCAI Computers and Thought Award, a trustee of IJCAII and a fellow of the AAAI.


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