Broad Area Colloquium For AI-Geometry-Graphics-Robotics-Vision
Markov chain Monte Carlo methods in AI
Computer Science Division
University of California, Berkeley
Monday, Apr 30, 2001, 4:15PM
Markov chain Monte Carlo (MCMC) methods have developed over the last few
years into a powerful tool for Bayesian statistics. In this talk I
will note in passing some close connections to recent Boolean satisfiability
algorithms, and then will consider some possible roles for MCMC in AI,
-- state estimation for dynamic Bayesian networks
-- large-scale data association (tracking many objects with many sensors)
-- inference in first-order probabilistic languages.
[Joint work with Hanna Pasula]
About the Speaker
Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in Physics from Oxford University in 1982, and his Ph.D. in Computer Science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is currently Professor of Computer Science. In 1990 he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was co-winner of the Computers and Thought Award, the highest international award in the field of artificial intelligence. He was a 1996 Miller Professor of the University of California, and became Chancellor's Professor in 2000. In 1998 he gave the Forsythe Memorial Lectures at Stanford University. He is a Fellow of the American Association for Artificial Intelligence and a member of its Executive Council. He is currently an Associate Editor of the Journal of the ACM.
His research interests include machine learning, limited rationality, real-time decision-making, intelligent agent architectures, autonomous vehicles, search, game-playing, reasoning under uncertainty, and commonsense knowledge representation. He has published over one hundred papers and three books, ``The Use of Knowledge in Analogy and Induction'' (Pitman, 1989), ``Do the Right Thing: Studies in Limited Rationality'' (MIT Press, 1991), and most recently ``Artificial Intelligence: A Modern Approach'' (Prentice Hall, 1995), which is the leading textbook in the field.
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