Here’s an imaginary scenario:
you’re a law enforcement officer confronted with a 21-year-old male suspect who
is accused of breaking into a private house on Sunday evening and stealing a
laptop, jewelry, and some cash. Your job is to find out whether the suspect has
an alibi and if so whether it is coherent and believable. That’s exactly the
kind of scenario that police officers the world over face on a regular basis.
But how do you train for such a situation? How do you learn the skills
necessary to gather the right kind of information? An increasingly common way
of doing this is with serious games, those designed primarily for purposes
other than entertainment. In the last 10 years or so, medical, military, and
commercial organizations all over the world began to experiment with game-based
scenarios that are designed to teach people how to perform their jobs and tasks
in realistic situations. But there is a problem with serious games which
require realistic interaction with another person. It’s relatively straightforward
to design one or two scenarios that are coherent, lifelike, and believable but
it’s much harder to generate them continually on an ongoing basis.
Imagine in the example above that
the suspect is a computer-generated character. What kind of activities could he
describe that would serve as a believable, coherent alibi for Sunday evening?
And how could he do it a thousand times, each describing a different realistic
alibi. Therein lies the problem. Researchers at Bar-Ilan University in Israel, claim
they’ve solved this problem. They have come up with a novel way of generating
ordinary, realistic scenarios that can be cut and pasted into a serious game to
serve exactly this purpose. The secret sauce in their new approach is to crowd-source
the new scenarios from real people using Amazon’s Mechanical Turk service. The
approach is straightforward. Researchers ask a set of questions asking what
they did during each one-hour period throughout various days, offering bonuses
to those who provide the most varied detail. They then analyze the answers,
categorizing activities by factors such as the times they are performed, the
age and sex of the person doing it, the number of people involved, and so on. This
then allows a computer game to cut and paste activities into the action at
appropriate times.
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