Distributed agent-based air traffic flow management

Abstract

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).

An agent-based approach for structured modeling, analysis and improvement of safety culture

“…Agent-based modeling and analysis have been used previously to study the efficiency or safety of air traffic scenarios (e.g. Wolfe 2007;Tumer and Agogino 2007;Blom et al 2006), however agent-based modeling at the organizational level to study safety culture is a new subject both in air traffic management and in the area of multi-agent systems.…”

“…Furthermore, another promising direction for future research is to integrate the developed approach into existing agent-based approaches such as Tumer and Agogino (2007) and Blom et al (2006), which address systems at the operational level and do not consider the organizational and cultural contexts. In such a way explicit reciprocal relations between organizational structures, processes and safety culture on the one side and operational safety indicators (e.g., risks) on the other side can be defined.…”

“…In particular in Tumer and Agogino (2007) a distributed agent-based air traffic flow management system is described. Through reinforcement learning this system ensures observance of separation safety norms by aircraft in a simulated airspace.…”

Design of semi-decentralized control laws for distributed-air-jet micromanipulators by reinforcement learning

“…In recent years there has been increased interest in decentralized approaches combined with machine learning techniques to solve complex real world problems [1], [2], [3]. A decentralized point of view offers several potential advantages in scalability and robustness, and can overcome the complexity constraints of conventional central control.…”

“…This project federates five French research teams and a Japanese one from five laboratories 2 . The device to control is an array of microelectro-valves able to produce controlled and directed microair-jets (cf.…”