[Intelligence Seminar] Nov. 29, 3:30pm:, Presentation by Gerry Tesauro (IBM Research)

Dana Houston dhouston at cs.cmu.edu
Mon Nov 28 08:40:07 EST 2011

Please join us for the next Intelligence Seminar!

Tuesday, November 29, 2011
Gates Building 4303

Host: Burr Settles, for appointments please contact Dana Houston 
(dhouston at cs.cmu.edu <mailto:dhouston at cs.cmu.edu>)

Speaker: Gerry Tesauro, IBM Research

Title: How Watson Learns Superhuman Jeopardy! Strategies

Abstract: Major advances in Question Answering technology were needed 
for Watson to play Jeopardy! at championship level--the show requires 
rapid-fire answers to challenging natural language questions, broad 
general knowledge, high precision, and accurate confidence estimates. In 
addition, Jeopardy! features four types of decision making carrying 
great strategic importance: (1) selecting the next clue when in control 
of the board; (2) deciding whether to attempt to buzz in; (3) wagering 
on Daily Doubles; (4) wagering in Final Jeopardy. This talk describes 
how Watson makes the above decisions using innovative quantitative 
methods that, in principle, maximize Watson's overall winning chances. 
We first describe our development of faithful simulation models of human 
contestants and the Jeopardy! game environment. We then present specific 
learning/optimization methods used in each strategy algorithm: these 
methods span a range of popular AI research topics, including Bayesian 
inference, game theory, Dynamic Programming, Reinforcement Learning, and 
real-time "rollouts." Application of these methods yielded superhuman 
game strategies for Watson that significantly enhanced its overall 
competitive record.

Joint work with David Gondek, Jon Lenchner, James Fan, and John Prager

Gerald Tesauro is a Research Staff Member at IBM's TJ Watson Research 
Center. He is best known for developing TD-Gammon, a self-teaching 
neural network that learned to play backgammon at human world 
championship level. He has also worked on theoretical and applied 
machine learning in a wide variety of other settings, including 
multi-agent learning, dimensionality reduction, computer virus 
recognition, computer chess (Deep Blue), intelligent e-commerce agents, 
and autonomic computing. Dr. Tesauro received BS and PhD degrees in 
physics from University of Maryland and Princeton University, respectively.
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