[Ml-stat-talks] Colloquioum Speaker Gerry Tesauro Wed Oct 26 4:30pm

Robert Schapire schapire at CS.Princeton.EDU
Mon Oct 24 10:47:06 EDT 2011

Again, if you want to meet with Gerry, please do sign up or contact Nicole.


On 10/20/2011 2:54 PM, Robert Schapire wrote:
> Gerry Tesauro (known for his work on TD-gammon, a championship-level 
> backgammon program, and more recently, on the Jeopardy-playing 
> program, Watson) will be visiting next Wednesday, 10/26, and through 
> the next morning.  See talk announcement below.
> If you would like to meet with him, please contact Nicole Wagenblast 
> <nwagenbl at CS.Princeton.EDU>, x8-4624.
> Rob
> How Watson Learns Superhuman Jeopardy! Strategies
> Gerry Tesauro, IBM Research
> Wednesday, October 26, 2011, 4:30 PM
> Computer Science Small Auditorium (Room 105)
> 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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schapire at cs.princeton.edu

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