Reinforcement Learning to Support Meta-Level Control in Air Traffic Management

Abstract

In a complex environment where the messages exchange intensively among the agents, a difficulty task is to decide the best action for new arriving messages during on-line control. A typical form of communication in Air Traffic Flow Management (ATFM) is verbal language to exchange the information and there is almost no digital recording of this communication between pilot and air controller. As the development of distributed computation system [1], the digital message communication is proposed especially for processing the immense amount of messages. In a real case such as the First Integrated Center of Air Defense and Air Traffic Control - CINDACTA I, in Brasilia, the system monitors 70% of the traffic flow in Brazil. According to the air traffic control procedure in CINDACTA I, Flight Information Region of Brasilia - FIR-BS is divided into 14 sectors and managed by 3 regional supervisors (Sao Paulo, Rio de Janeiro and Brasilia). Every air controller monitors his sector and is responsible to his supervisor. Only supervisor can make a decision to manage the air traffic flow. Any action is realized by air controller according to the decision of supervisor. For example, a decision may be to hold a flight in an airport for more 10 minutes, or assign the priority to another flight in the landing processing. In CINDACTA I, the monitor system (equipment and operation software) is suitable, but there is no a general system to manage and synchronize the traffic dynamically and to support the decision for adequate traffic management. Supervisor makes the decision just by his experience without quantity analyses of the impact of the action. To resolve this kind of problem, some researches were developed using the solution of Artificial Intelligence and others according to the new conception of ATFM. A distributed ATM system has been studied in Australia [2]. The advantages of that approach are inherent distribution, autonomy, communication and reliability. Prevot, from NASA Ames Research Center, has studied a distributed approach for operator interfaces and intelligent flight guidance, management and decision support [3]. An application of multi-agent coordination techniques in ATM, which sets up a methodological framework using multi-agent coordination techniques that supports the collaborative work in ATM has also been presented recently by Eurocontrol [4]. It should be mentioned that the multi-agen

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http://dx.doi.org/10.5772/5293