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

Dana Houston dhouston at cs.cmu.edu
Tue Nov 22 14:48:14 EST 2011

NOVEMBER 29 AT 3:30PM, IN GHC 4303

Host: Burr Settles
For meetings, contact Dana Houston (dhouston at cs.cmu.edu)


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.


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.

Dana M. Houston
Language Technologies Institute
School of Computer Science
Carnegie Mellon University
5405 Gates Hillman Complex
5000 Forbes Avenue
Pittsburgh, PA 15213

T:  (412)268-4717
F:  (412)268-6298

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