Friday, September 14, 2007

Collective intelligence in air traffic control Kagan Tumer now at Oregon State University
Dr. Turner's recent application of COIN to traffic control problem:

Air traffic flow management is one of the fundamental challenges
facing the Federal Aviation Administration (FAA)
today. The FAA estimates that in 2005 alone, there were
over 322,000 hours of delays at a cost to the industry in excess
of three billion dollars. Finding reliable and adaptive
solutions to the flow management problem is of paramount
importance if the Next Generation Air Transportation Systems
are to achieve the stated goal of accommodating three
times the current traffic volume. This problem is particularly
complex as it requires the integration and/or coordination
of many factors including: new data (e.g., changing
weather info), potentially conflicting priorities (e.g., different
airlines), limited resources (e.g., air traffic controllers)
and very heavy traffic volume (e.g., over 40,000 flights over
the US airspace).
In this paper we use FACET – an air traffic flow simulator
developed at NASA and used extensively by the FAA and
industry – to test a multi-agent algorithm for traffic flow
management. An agent is associated with a fix (a specific
location in 2D space) and its action consists of setting the
separation required among the airplanes going though that
fix. Agents use reinforcement learning to set this separation
and their actions speed up or slow down traffic to manage
congestion. Our FACET based results show that agents receiving
personalized rewards reduce congestion by up to 45%
over agents receiving a global reward and by up to 67% over
a current industry approach (Monte Carlo estimation).

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