we have designed so far have focused
on problems that are easily divided
into subtasks. The “bite-size” nature
of these games adds to their popularity and appeal to casual gamers in particular, since such players typically go
for games they can play “just one more
time” without having to make too much
of a time commitment.
The GWAP approach represents a
promising opportunity for everyone to
contribute to the progress of AI. By leveraging the human time spent playing
games online, GWAP game developers
are able to capture large sets of training data that express uniquely human
perceptual capabilities. This data can
contribute to the goal of developing
computer programs and automated
systems with advanced perceptual or
We would like to thank Manuel and
Lenore Blum, Mike Crawford, Shiry
Ginosar, Severin Hacker, Susan Hrish-enko, Mihir Kedia, Edith Law, Bryant
Lee, and Roy Liu for their help in this
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This work was partially supported by National
Science Foundation grants CCR-0122581 and
CCR-0085982 (ALADDIN) and generous gifts
from Google, Inc., and the Heinz Endowment.
Luis von Ahn was partially supported by
a Microsoft Research Graduate Fellowship,
a Microsoft Research New Faculty Fellowship,
and a MacArthur Fellowship.
Luis von Ahn ( email@example.com) is an assistant
professor in the Computer Science Department at
Carnegie Mellon University, Pittsburgh, PA.
Laura Dabbish ( firstname.lastname@example.org) is an
assistant professor of information technology and
organizations in the Heinz School, with a joint appointment
in the Human-Computer Interaction Institute in
the School of Computer Science at Carnegie Mellon
University, Pittsburgh, PA.
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