Dear all,

This is an early reminder of the talk by Dr. Gerald Tesauro from IBM Research Center on *5 Dec *(Friday) starting from 13.30 at AA3195. He will tell us about the exciting story behind how Watson beat human competitors on Jeopardy!.

Best, - Cho

P.S. From now on, you can check the schedules of the future (and past) seminars at http://lisa.iro.umontreal.ca/seminars_en.html. The same page in French will appear soon.

=== - Speaker: Dr. Gerald Tesauro (IBM TJ Watson Research Center) - Date/Time: 13.30 - 14.30, 5 Dec - Place: AA3195 - 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.

- Speaker Bio: Gerald Tesauro is a Research Staff Member at IBM's TJ Watson Research Center. He is best known for creating 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 serves on the NIPS Foundation Board of Directors, and was recently elected AAAI Fellow.