A pancake-shaped vacuum that cleans an entire room
without guidance, sensing and avoiding drop-offs like stairs. An R2-D2
sized cart that navigates hospital hallways to deliver patient samples
to a lab. Airline computers that give callers updated flight information.
Devices driven by artificial intelligence (AI) are
becoming part of the fabric of life. But these gadgets seem simple-minded
compared to the ones Peter Stone envisions. The assistant professor
in the Department of
Computer Sciences at The University of Texas
at Austin is laying the groundwork for a not-too-distant future populated
by
adaptable
AI devices that not only can improve their interactions with objects
around them, but affect each other’s actions as well.
“It’s not out of the question that in
20 years we’ll be seeing cars that drive themselves,” Stone
says, noting that the cars’ abilities to communicate with each
other would permit rapid, accident-free travel.
“Creating those cars is a multi-agent systems
problem,” he says. “It’s about learning how to develop
independently controlled cars that will drive themselves by coordinating
around streets, which will be a limited resource. That’s at
the core of all the research problems I look at.”
His chosen medium of research is robot soccer. He
and students prepare teams to compete in international competitions.
An avid soccer player since childhood, Stone was
on the varsity team at the University of Chicago, and plays on a top-notch
amateur team in Austin. He started working robot soccer players and
computer simulations in graduate school at Carnegie Mellon University.
Stone
described how he created the 1999 simulation team that won eight games
with a combine score of 119-0 in his book “Layered Learning in Multiagent
Systems: A Winning Approach to Robotic Soccer.” He also helped edit a
similar book on the 2000 RoboCup competition, but notes that “the competitions
are a good motivating tool, but not the be-all and end-all of it.”
Stone’s current effort focuses on Aibo robots
from Sony that have well-defined hardware, permitting him and his
students to focus entirely on computer programming.
In preparing for
a competition last year, Stone and 19 graduate students and an undergraduate
worked doggedly to improve the robots’ abilities. One
group led by graduate student Mohan Sridharan, focused on teaching the robots,
known as the UT Austin Villa team, to distinguish the pattern of numbers on
their video screens representing the pastel colors of the goals, the green
of their 2.1-meter by 4.5-meter playing field, the colorful outfits of teammates
and other objects.
Another group worked to improve the robots’ jerky
walks, and another to guide them through the steps of scoring a goal:
find the ball, walk to the ball, find the goal, square up the ball
with the goal, re-check that everything’s aligned and then shoot
with a one-legged lunging kick or by popping front legs back together.
The reverse of the latter, front power kick was taught to a “goalie” to
block a ball.
In addition, the Aibos were taught to play without
human input based on instructions built into their code before the
games. For example, the “Dibs” program allowed an Austin
Villa robot close to the ball to stake a claim to it, using an Ethernet
connection to communicate that decision to teammates.
Though Stone always considers these more practical
applications of autonomous machinery, he admits to feeling a rush
when considering RoboCup creator Hirokai Kitano’s ultimate vision:
creating a team of autonomous humanoid robots by 2050 that compete
and win against a real soccer team.
“That’s akin to the dream of landing
a man on the moon,” Stone says, noting that this dream will
keep AI researchers challenged for decades.
Other AI dreams in their infancy include robots that
can perform search and rescue operations for military or other purposes,
and a network of computers whose nodes can sense outages and autonomously
decide the quickest ways to re-route e-mail.
As the dreams become reality, Stone notes that people
will tend to forget the machine learning behind them. Consider, for
example, the case of shoppers who think nothing about AI when they
go to a Web site and receive suggestions of other products to purchase,
or chess-playing programs that make moves without input. There is
an advantage to these fluid expectations, though.
“It means we have the freedom to keep looking
20 years into the future,” Stone says.
