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 questionanswering
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
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.