1 Billion Pages = 1 Million Dollars? Mining the Web to Play "Who Wants to be a Millionaire?"
Shyong (Tony) Lam, David Pennock, Dan Cosley, Steve Lawrence
We exploit the redundancy and volume of information on the web to build a computerized player for the ABC TV game show ``Who Wants To Be A Millionaire?' The player consists of a question-answering module and a decision-making module. The question-answering module utilizes question transformation techniques, natural language parsing, multiple information retrieval algorithms, and multiple search engines; results are combined in the spirit of ensemble learning using an adaptive weighting scheme. Empirically, the system correctly answers about 75% of questions from the Millionaire CD-ROM, 3rd edition -- general-interest trivia questions often about popular culture and common knowledge. The decision-making module chooses from allowable actions in the game in order to maximize expected risk-adjusted winnings, where the estimated probability of answering correctly is a function of past performance and confidence in in correctly answering the current question. When given a six question head start (i.e., when starting from the $2,000 level), we find that the system performs about as well on average as humans starting at the beginning. Our system demonstrates the potential of simple but well-chosen techniques for mining answers from unstructured information such as the web
PDF Link: /papers/03/p337-lam.pdf
AUTHOR = "Shyong (Tony) Lam
and David Pennock and Dan Cosley and Steve Lawrence",
TITLE = "1 Billion Pages = 1 Million Dollars? Mining the Web to Play "Who Wants to be a Millionaire?"",
BOOKTITLE = "Proceedings of the Nineteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-03)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2003",
PAGES = "337--345"